查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Research findings on Nanotechnology - Nanofluids are discussed in a new report. According to news reporting out of Sirsa, India, by NewsRx editors, research stated, “PurposeThe purpose of this paper is to show case the utilization of the magnetohydrodynamics-microrotating Casson’s nanoflui d flow model (MHD-MRCNFM) in examining the impact of an inclined magnetic field within a porous medium on a nonlinear stretching plate. This investigation is co nducted by using neural networking techniques, specifically using neural network s-backpropagated with the Levenberg-Marquardt scheme (NNBLMS). Design/methodolog y/approachThe initial nonlinear coupled PDEs system that represented the MRCNFM is transformed into an analogous nonlinear ODEs system by the adoption of simila rity variables.”
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Machine Learning is th e subject of a report. According to news reporting out of London, United Kingdom , by NewsRx editors, research stated, “This study introduces a machine learning (ML)-assisted image segmentation method for automatic bubble identification in g as-solid quasi-2D fluidized beds, offering enhanced accuracy in bubble recogniti on. Binary images are segmented by the ML method, and an in-house Lagrangian tra cking technique is developed to track bubble evolution.” Our news journalists obtained a quote from the research from University College London (UCL), “The ML-assisted segmentation method requires few training data, a chieves an accuracy of 98.75%, and allows for filtering out common sources of uncertainty in hydrodynamics, such as varying illumination conditions and out-of-focus regions, thus providing an efficient tool to study bubbling in a standard, consistent, and repeatable manner. In this work, the ML-assisted me thodology is tested in a particularly challenging case: structured oscillating f luidized beds, where the spatial and time evolution of the bubble position, velo city, and shape are characteristics of the nucleation-propagation-rupture cycle. The new method is validated across various operational conditions and particle sizes, demonstrating versatility and effectiveness. It shows the ability to capt ure challenging bubbling dynamics and subtle changes in velocity and size distri butions observed in beds of varying particle size. New characteristic features o f oscillating beds are identified, including the effect of frequency and particl e size on the bubble morphology, aspect, and shape factors and their relationshi p with the stability of the flow, quantified through the rate of coalescence and splitting events.”
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Machine Learning is th e subject of a report. According to news reporting originating in Naples, Italy, by NewsRx journalists, research stated, “To assess the efficacy of machine lear ning and radiomics analysis by computed tomography (CT) in presurgical setting, to predict RAS mutational status in colorectal liver metastases. Patient selecti on in a retrospective study was carried out from January 2018 to May 2021 consid ering the following inclusion criteria: patients subjected to surgical resection for liver metastases; proven pathological liver metastases; patients subjected to enhanced CT examination in the presurgical setting with a good quality of ima ges; and RAS assessment as standard reference.” The news reporters obtained a quote from the research from the Division of Radio logy, “A total of 851 radiomics features were extracted using the PyRadiomics Py thon package from the Slicer 3D image computing platform after slice-by-slice se gmentation on CT portal phase by two expert radiologists of each individual live r metastasis performed first independently by the individual reader and then in consensus. Balancing technique was performed, and inter- and intraclass correlat ion coefficients were calculated to assess the between-observer and within-obser ver reproducibility of features. Receiver operating characteristics (ROC) analys is with the calculation of area under the ROC curve (AUC), sensitivity (SENS), s pecificity (SPEC), positive predictive value (PPV), negative predictive value (N PV) and accuracy (ACC) were assessed for each parameter. Linear and non-logistic regression model (LRM and NLRM) and different machine learning-based classifier s were considered. Moreover, features selection was performed before and after a normalized procedure using two different methods (3-sigma and z-score). Seventy -seven liver metastases in 28 patients with a mean age of 60 years (range 40-80 years) were analyzed. The best predictors, at univariate analysis for both norma lized procedures, were original_shape_Maximum2DDiamete r and wavelet_HLL_glcm_InverseVariance th at reached an accuracy of 80%, an AUC 0.75, a sensitivity 80% and a specificity 70% (p value <<0.01). However, a multivariate analysis significantly increased the accuracy in RAS prediction when a linear regression model (LRM) was used. The best performa nce was obtained using a LRM combining linearly 12 robust features after a z-sco re normalization procedure: AUC of 0.953, accuracy 98%, sensitivity 96%, specificity of 100%, PPV 100% and NPV 96% (p value <<0.01 ). No statistically significant increase was obtained considering the tested mac hine learning both without normalization and with normalization methods.”
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Current study results on Machine Learn ing have been published. According to news reporting out of Changsha, People’s R epublic of China, by NewsRx editors, research stated, “Titanium (Ti) alloys are widely used in high-tech fields like aerospace and biomedical engineering. Laser additive manufacturing (LAM), as an innovative technology, is the key driver fo r the development of Ti alloys.” Financial supporters for this research include Singapore RIE 2025 MTC, Young Ind ividual Research Grants, Agency for Science Technology & Research (A*STAR), National Natural Science Foundation of China (NSFC), National Science Foundation (NSF).
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on Ro botics. According to news reporting out of Lille, France, by NewsRx editors, res earch stated, “Soft and hyperelastic materials possess properties of resilience and flexibility, characterizing a class of soft-continuum manipulators (SCMs). T he latter describes a robot structure with an infinite number of degrees of free dom (DoF), useful for mobility and manipulation.” Financial support for this research came from CRIStAL Laboratory of Lille, Franc e. Our news journalists obtained a quote from the research from National Center for Scientific Research (CNRS), “However, these geometric characteristics are a sou rce of modeling and control problems. In this article, a Pythagorean hodograph ( PH) curve-based reduced order model (ROM) relying on the Gauss- Lobatto quadratur e is investigated for the modeling and the control of SCM. This allows, first, r educing the dimension of the SCM kinematics based on the PH parametric curves wi th a predefined length, and second, developing the shape kinematics control from its control polygon. The use of the Gauss-Lobatto quadrature allows to move ind ependently the PH curve control points, while preserving PH features of length a nd minimum curve energy. These features are important to control in real-time th e shape of the SCM.”
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Artificial Intelligenc e is the subject of a report. According to news reporting originating in Vilnius , Lithuania, by NewsRx journalists, research stated, “The conceptual definition of epilepsy has been changing over decades and remains debatable. We assessed ho w artificial intelligence (AI) conceives epilepsy and its impact on a person’s l ife through verbal and visual material.” The news reporters obtained a quote from the research from Vilnius University, “ We asked the Chat Generative Pre-Trained Transformer (ChatGPT, OpenAI) to define epilepsy and its impact. Prompts from ChatGPT were transferred to another AI to ol DALL·E 2 (Open AI) to generate visual images based on verbal input. The ChatG PT definition on epilepsy relied on both its conceptual and practical definition s. It titled epilepsy to be ‘a neurological disorder characterized by recurring seizures’ that has significant impact on patients’ lives and is diagnosed after two or more unprovoked seizures or if there is a high risk of future seizures. C hatGPT presented nine issues - seizure-related injuries, limitations on daily ac tivities, emotional and psychological impact, social stigma and isolation, educa tional and employment challenges, relationship and family dynamics, medication s ide effects, financial burden, and coexisting conditions - as major consequences of epilepsy. AI-generated images ranged from direct portrayals of these phenome na to abstract imagery but were mostly deprived of symbolic elements and visual metaphors. We showed that AI can identify and visually interpret the burden of e pilepsy from medical, societal and economical perspectives. However, the imagery created is not figurative and does not follow allegorical narratives put forwar d by epilepsy specialists in similar studies.”
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators discuss new findings in artificial intelligence. According to news originating from the University of Ca lifornia Santa Cruz by NewsRx correspondents, research stated, “In mainstream ma chine learning, transformers are gaining widespread usage.” The news journalists obtained a quote from the research from University of Calif ornia Santa Cruz: “As Vision Transformers rise in popularity in computer vision, they now aim to tackle a wide variety of machine learning applications. In part icular, transformers for High Energy Physics (HEP) experiments continue to be in vestigated for tasks including jet tagging, particle reconstruction, and pile-up mitigation. An improved Quantum Vision Transformer (QViT) with a quantum-enhanc ed self-attention mechanism is introduced and discussed. A shallow circuit is pr oposed for each component of self-attention to leverage current Noisy Intermedia te Scale Quantum (NISQ) devices. Variations of the hybrid architecture/model are explored and analyzed. The results demonstrate a successful proof of concept fo r the QViT, and establish a competitive performance benchmark for the proposed d esign and implementation.”
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Researchers detail new data in Robotic s - Robotics and Automation. According to news originating from Ulm, Germany, by NewsRx correspondents, research stated, “Adverse weather conditions can severel y affect the performance of LiDAR sensors by introducing unwanted noise in the m easurements. Therefore, differentiating between noise and valid points is crucia l for the reliable use of these sensors.” Our news journalists obtained a quote from the research from Ulm University, “Cu rrent approaches for detecting adverse weather points require large amounts of l abeled data, which can be difficult and expensive to obtain. This letter propose s a label-efficient approach to segment LiDAR point clouds in adverse weather. W e develop a framework that uses few-shot semantic segmentation to learn to segme nt adverse weather points from only a few labeled examples. Then, we use a semi- supervised learning approach to generate pseudo-labels for unlabelled point clou ds, significantly increasing the amount of training data without requiring any a dditional labeling. We also integrate good weather data in our training pipeline , allowing for high performance in both good and adverse weather conditions. Res ults on real and synthetic datasets show that our method performs well in detect ing snow, fog, and spray.”
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Transplant Medicine - Kidney Transplants is the subject of a report. According to news originating fro m Rotterdam, Netherlands, by NewsRx correspondents, research stated, “Research o n solid organ transplantation has taken advantage of the substantial acquisition of medical data and the use of artificial intelligence (AI) and machine learnin g (ML) to answer diagnostic, prognostic, and therapeutic questions for many year s. Nevertheless, despite the question of whether AI models add value to traditio nal modeling approaches, such as regression models, their ‘black box’ nature is one of the factors that have hindered the translation from research to clinical practice.” Our news journalists obtained a quote from the research from University Medical Center Rotterdam, “Several techniques that make such models understandable to hu mans were developed with the promise of increasing transparency in the support o f medical decision-making. These techniques should help AI to close the gap betw een theory and practice by yielding trust in the model by doctors and patients, allowing model auditing, and facilitating compliance with emergent AI regulation s. But is this also happening in the field of kidney transplantation? This revie w reports the use and explanation of ‘black box’ models to diagnose and predict kidney allograft rejection, delayed graft function, graft failure, and other rel ated outcomes after kidney transplantation. In particular, we emphasize the disc ussion on the need (or not) to explain ML models for biological discovery and cl inical implementation in kidney transplantation.”
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – New research on Machine Learning is the subject o f a report. According to news reporting originating in Johannesburg, South Afric a, by NewsRx journalists, research stated, “Chemical Looping Combustion (CLC) is a carbon capture technology that uses an oxygen carrier to transfer the oxidizi ng agent to the fuel for combustion. This study used different machine learning algorithms, Artificial neural network and Response surface methodology to estima te the surface region process performance and optimize the process condition for the CLC of different solid fuels waste paper, plastic waste, and sugarcane baga sse blends.” The news reporters obtained a quote from the research from the University of Joh annesburg, “Based on the combustion efficiency, CO2 yield and CO2 capture effici ency responses, A high performance correlation (R-2 > 0. 8) was obtained for all the combustion parameters analyzed. The perturbation plo t derived from the RSM analysis indicated that the most significant input parame ters include the steam to fixed carbon, blend ratio and the fuel reaction temper ature. The CLC process was optimized using RSM. For blends of SCB/WP, the best o perating conditions were found to be 800 degrees C, a solid flow rate of 197.7 k g/h, an oxygen carrier to fuel ratio of 1.1, a steam to fixed carbon ratio of 2. 16, and a blend ratio of 1. Similarly, for blends of SCB/PW, the optimal operati ng conditions were 800 degrees C, a solid flow rate of 199.4 kg/h, an oxygen car rier to fuel ratio of 1.3, steam to fixed carbon ratio of 2, and a blend ratio o f 0.3.”
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