查看更多>>摘要：Investigators publish new report on artificial intelligence. According to news originating from the University of Science and Technology Beijing by NewsRx correspondents, research stated, “Machine learning is a powerful means for the rapid development of high-performance functional materials.” Our news reporters obtained a quote from the research from University of Science and Technology Beijing: “In this study, we presented a machine learning workflow for predicting the corrosion resistance of a self-healing epoxy coating containing ZIF-8@Ca microfillers. The orthogonal Latin square method was used to investigate the effects of the molecular weight of the polyetheramine curing agent, molar ratio of polyetheramine to epoxy, molar content of the hydrogen bond unit (Upy-D400), and mass content of the solid microfillers (ZIF-8@Ca microfillers) on the low impedance modulus (lg|Z|0.01Hz) values of the scratched coatings, generating 32 initial datasets. The machine learning workflow was divided into two stages: In stage I, five models were compared and the random forest (RF) model was selected for the active learning. After 5 cycles of active learning, the RF model achieved good prediction accuracy: coefficient of determination (R 2) = 0.709, mean absolute percentage error (MAPE) = 0.081, root mean square error (RMSE) = 0.685 (lg(O·cm2)). In stage II, the best coating formulation was identified by Bayesian optimization. Finally, the electrochemical impedance spectroscopy (EIS) results showed that compared with the intact coating ((4.63 ± 2.08) x 1011 O·cm2), the |Z|0.01Hz value of the repaired coating was as high as (4.40 ± 2.04) x 1011 O·cm2.”
查看更多>>摘要：Current study results on have been published. According to news originating from Chengdu, People’s Republic of China, by NewsRx correspondents, research stated, “This paper proposes a diagnosis method based on time series and support vector machine (SVM) to improve the timeliness, accuracy, and feasibility of fault diagnosis for photovoltaic (PV) arrays.” Funders for this research include International Scientific And Technological Cooperation Projects of Chengdu City. Our news editors obtained a quote from the research from Chengdu Technological University: “It obtains the nominal output power of the PV array based on real-time collected data such as voltage, current, radiation, and temperature and normalizes the power values at different time points throughout the day to form a time series. Using the time series values as input data for a “one-to-one” multiclass classifier, we can identify and classify typical operational faults such as random shading, fixed shading, and aging degradation of PV arrays.”
查看更多>>摘要：Current study results on artificial intelligence have been published. According to news reporting out of Maharashtra, India, by NewsRx editors, research stated, “This narrative review explores the evolving role of artificial intelligence (AI) in haemodynamic monitoring, emphasising its potential to revolutionise patient care.” Our news correspondents obtained a quote from the research from Homi Bhabha National Institute: “The historical reliance on invasive procedures for haemodynamic assessments is contrasted with the emerging non-invasive AI-driven approaches that address limitations and risks associated with traditional methods. Developing the hypotension prediction index and introducing CircEWSTM and CircEWS-lite TM showcase AI’s effectiveness in predicting and managing circulatory failure. The crucial aspects include the balance between AI and healthcare professionals, ethical considerations, and the need for regulatory frameworks. The use of AI in haemodynamic monitoring will keep growing with ongoing research, better technology, and teamwork.”
查看更多>>摘要：Fresh data on Machine Learning are presented in a new report. According to news reporting originating in West Bengal, India, by NewsRx journalists, research stated, “Reviews and ratings of consumers towards a product impact consumer decision-making and their perceptions. Such information is key in measuring consumer satisfaction and net promoter scores.” The news reporters obtained a quote from the research from the Indian Institute of Technology (IIT) Kharagpur, “However, when the reviewed products are refurbished, consumer reviews become more important because information influences consumer behaviour and attitude toward looped products. This research explores the decision-influencing attributes of consumers while purchasing refurbished goods using quantitative and qualitative methods. Online after-sales 1986 laptop customers’ review and rating data in the public domain were analysed to reveal the decision-influencing attributes and their impact on potential consumers. The study envisions assisting the operations of sellers in the refurbished market by strengthening their businesses’ value proposition and stimulating reverse logistics entrepreneurs to use the opportunity. Review data containing lifecycle valuation of old laptops induced feature extraction by machine learning applications. It is beneficial to sellers in the refurbished product segment. It provides information to strengthen their value proposition and is informative to entrepreneurs wanting to enter the segment. Based on the text analysis of consumer reviews, the study’s results show that price, brand, design, performance, services, and utility influence consumers. The frequency analysis technique was used to extract attributes, followed by content analysis and feature selection using SHapley Additive exPlanations (SHAP) for exploring correlations between features and star ratings. Lastly, multinomial logistic regression was used to validate the generated model. The results show that brand, design, price, and utility are the most prominent attributes influencing consumers’ decision-making with positive sentiments.”
查看更多>>摘要：Investigators discuss new findings in Robotics. According to news reporting out of Beijing, People’s Republic of China, by NewsRx editors, research stated, “In practical applications of collaborative robots, proximity and pressure sensing capabilities are crucial for ensuring the safety and effectiveness of interaction. This paper proposes a novel method of integrating a bimodal sensor that uses a piezoresistive membrane as a self-capacitive electrode, overcoming the current challenges in integration and signal independence.” Financial supporters for this research include National Natural Science Foundation of China (NSFC), Open Laboratory Concept Verification Project of Zhongguancun National Demonstration Zone, Outstanding Research Project of Shen Yuan Honors College, BUAA.
查看更多>>摘要：Current study results on Machine Learning have been published. According to news reporting originating from Jamshedpur, India, by NewsRx correspondents, research stated, “Manipulation of creep properties and microstructural transformations at different temperatures and applied stresses depicts huge importance for the design and development of various grades of metals and alloys. Therefore, we have considered nano-size face-centered cubic (FCC) single crystal of Fe-Cr-Ni alloy to investigate creep response under a wide range of temperatures and pressure through molecular dynamics (MD) simulation and regression-based machine learning methodologies.” Financial support for this research came from CSIR-NML. Our news editors obtained a quote from the research from the Council of Scientific and Industrial Research (CSIR), “From MD simulation, we have found the evolution of multiple rectangular blocks of body-centered cubic (BCC) crystal and layered FCC and HCP crystal during creep deformation under externally applied tensile load. Rectangular blocks and layered crystal structures corroborated with the secondary and tertiary stages of creep curves of Fe-Cr-Ni alloy, respectively. Machine learning methodology provides information to predict the creep properties and correlates data obtained from MD simulations.”
查看更多>>摘要：New research on Robotics is the subject of a report. According to news reporting out of Beijing, People’s Republic of China, by NewsRx editors, research stated, “Generating an accurate and continuous semantic occupancy map is a key component of autonomous robotics. Most existing continuous semantic occupancy mapping methods neglect the potential differences between voxels, which reconstruct an overinflated map.” Financial supporters for this research include National Natural Science Foundation of China (NSFC), CAST program, Collective Intelligence amp; Collaboration Laboratory. Our news journalists obtained a quote from the research from the Beijing Institute of Technology, “What is more, these methods have high computational complexity due to the fixed and large query range. To address the challenges of overinflation and inefficiency, this article proposes a novel sharp-edged and efficient continuous semantic occupancy mapping algorithm (SEE-CSOM). The main contribution of this work is to design the Redundant Voxel Filter Model (RVFM) and the Adaptive Kernel Length Model (AKLM) to improve the performance of the map. RVFM applies context entropy to filter out the redundant voxels with a low degree of confidence, so that the representation of objects will have accurate boundaries with sharp edges. AKLM adaptively adjusts the kernel length with class entropy, which reduces the amount of data used for training. Then, the multientropy kernel inference function is formulated to integrate the two models to generate the continuous semantic occupancy map.”
查看更多>>摘要：New research on Machine Learning is the subject of a report. According to news reporting originating from Tromso, Norway, by NewsRx correspondents, research stated, “This study aimed to predict fatigue 18 months post-stroke by utilizing comprehensive data from the acute and sub-acute phases after stroke in a machine-learning set-up. A prospective multicenter cohort-study with 18-month follow-up.” Our news editors obtained a quote from the research from the Arctic University of Norway (UiT), “Outpatient clinics at 3 university hospitals and 2 local hospitals. 474 participants with the diagnosis of acute stroke (mean (SD) age; 70.5 (11.3), 59% male). Not applicable. The primary outcome, fatigue at 18 months, was assessed using the Fatigue Severity Scale (FSS-7). FSS-7 5 was defined as fatigue. In total, 45 prediction variables were collected, at initial hospital-stay and 3-month post-stroke. The best performing model, random forest, predicted 69% of all subjects with fatigue correctly with a sensitivity of 0.69 (95% CI: 0.50, 0.86), a specificity of 0.74 (95% CI: 0.66, 0.83), and an Area under the Receiver Operator Characteristic curve of 0.79 (95% CI: 0.69, 0.87) in new unseen data. The proportion of subjects predicted to suffer from fatigue, who truly suffered from fatigue at 18-months was estimated to 0.41 (95% CI: 0.26, 0.57). The proportion of subjects predicted to be free from fatigue who truly did not have fatigue at 18-months was estimated to 0.90 (95% CI: 0.83, 0.96).”
查看更多>>摘要：Investigators publish new report on Robotics. According to news reporting out of Beijing, People’s Republic of China, by NewsRx editors, research stated, “This article studies the problem of controlling a multirobot system to achieve a polygon formation in a self-organized manner. Different from the typical formation control strategies where robots are steered to satisfy the predefined control variables, such as pair-wise distances, relative positions and bearings, the foremost idea of this article is to achieve polygon formations by injecting control inputs randomly to a few robots (say, vertex robots) of the group, and the rest follow the simple principles of moving toward the midpoint of their two nearest neighbors in the ring graph without any external inputs.” Financial supporters for this research include National Key Research and Development Program of China, National Natural Science Foundation of China (NSFC), Shanghai Municipal Science and Technology Major Project.
查看更多>>摘要：Researchers detail new data in Machine Learning. According to news reporting from Montreuil, France, by NewsRx journalists, research stated, “This paper introduces a predictive maintenance model based on Machine Learning (ML) in the context of a smart factory. It addresses a critical aspect within factories which is the health assessment of vital machinery.” The news correspondents obtained a quote from the research from the University Institute of Technology, “This case study specifically focuses on two brass accessories assembly robots and predicts the degradation of their power transmitters, which operate under severe mechanical and thermal conditions. The paper presents a predictive model based on ML and Artificial Intelligence (the Discrete Bayes Filter) to estimate and foresee the gradual deterioration of robots’ power transmitters. It aims at empowering operators to make informed decisions regarding maintenance interventions. The model is based on a Discrete Bayesian Filter (DBF) in comparison to a model based on Naive Bayes Filter (NBF). The findings indicate that the DBF model demonstrates superior predictive performance compared to the NBF model. The predictive model’s investigation results were validated during testing on robots.”
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