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    Research from Beihang University Yields New Study Findings on Robotics (Perceived Safety Assessment of Interactive Motions in Human-Soft Robot Interaction)

    94-95页
    查看更多>>摘要：Investigators discuss new findings in robotics. According to news reporting from Beijing, People's Republic of China, by NewsRx journalists, research stated, “Soft robots, especially soft robotic hands, possess prominent potential for applications in close proximity and direct contact interaction with humans due to their softness and compliant nature.” Financial supporters for this research include National Science Foundation For Excellent Young Scholars, China; National Science Foundation Support Projects, China. Our news journalists obtained a quote from the research from Beihang University: “The safety perception of users during interactions with soft robots plays a crucial role in influencing trust, adaptability, and overall interaction outcomes in human-robot interaction (HRI). Although soft robots have been claimed to be safe for over a decade, research addressing the perceived safety of soft robots still needs to be undertaken. The current safety guidelines for rigid robots in HRI are unsuitable for soft robots. In this paper, we highlight the distinctive safety issues associated with soft robots and propose a framework for evaluating the perceived safety in human-soft robot interaction (HSRI). User experiments were conducted, employing a combination of quantitative and qualitative methods, to assess the perceived safety of 15 interactive motions executed by a soft humanoid robotic hand. We analyzed the characteristics of safe interactive motions, the primary factors influencing user safety assessments, and the impact of motion semantic clarity, user technical acceptance, and risk tolerance level on safety perception.”
      原文链接:
    • NETL
    • NSTL
    • Mdpi

    China University of Geosciences Researchers Focus on Machine Learning (Estimation of Daily Maize Gross Primary Productivity by Considering Specific Leaf Nitrogen and Phenology via Machine Learning Methods)

    95-96页
    查看更多>>摘要：Research findings on artificial intelligence are discussed in a new report. According to news originating from Wuhan, People's Republic of China, by NewsRx correspondents, research stated, “Maize gross primary productivity (GPP) contributes the most to the global cropland GPP, making it crucial to accurately estimate maize GPP for the global carbon cycle. Previous research validated machine learning (ML) methods using remote sensing and meteorological data to estimate plant GPP, yet they disregard vegetation physiological dynamics driven by phenology.” Funders for this research include National Nature Science Foundation of China Program; Yinshanbeilu Grassland Eco-hydrology National Observation And Research Station, China Institute of Water Resources And Hydropower Research. Our news reporters obtained a quote from the research from China University of Geosciences: “Leaf nitrogen content per unit leaf area (i.e., specific leaf nitrogen (SLN)) greatly affects photosynthesis. Its maximum allowable value correlates with a phenological factor conceptualized as normalized maize phenology (NMP). This study aims to validate SLN and NMP for maize GPP estimation using four ML methods (random forest (RF), support vector machine (SVM), convolutional neutral network (CNN), and extreme learning machine (ELM)). Inputs consist of vegetation index (NDVI), air temperature, solar radiation (SSR), NMP, and SLN. Data from four American maize flux sites (NE1, NE2, and NE3 sites in Nebraska and RO1 site in Minnesota) were gathered. Using data from three NE sites to validate the effect of SLN and MMP shows that the accuracy of four ML methods notably increased after adding SLN and MMP. Among these methods, RF and SVM achieved the best performance of Nash-Sutcliffe efficiency coefficient (NSE) = 0.9703 and 0.9706, root mean square error (RMSE) = 1.5596 and 1.5509 gC·m-2·d-1, and coefficient of variance (CV) = 0.1508 and 0.1470, respectively. When evaluating the best ML models from three NE sites at the RO1 site, only RF and CNN could effectively incorporate the impact of SLN and NMP. But, in terms of unbiased estimation results, the four ML models were comprehensively enhanced by adding SLN and NMP.”
      原文链接:
    • NETL
    • NSTL
    • Mdpi

    South China University of Technology Reports Findings in Machine Learning (Prediction of effluent total nitrogen and energy consumption in wastewater treatment plants: Bayesian optimization machine learning methods)

    96-97页
    查看更多>>摘要：New research on Machine Learning is the subject of a report. According to news reporting originating from Guangzhou, People's Republic of China, by NewsRx correspondents, research stated, “The control of effluent total nitrogen (TN) and total energy consumption (TEC) is a key issue in managing wastewater treatment plants. In this study, effluent TN and TEC predictive models were established by selecting influent water quality and process control indicators as input features.” Our news editors obtained a quote from the research from the South China University of Technology, “The prediction performance of machine learning methods under different random seeds was explored, the moving average method was used for data amplification, and the Bayesian algorithm was used for hyperparameter optimization. The results showed that compared with the traditional hyperparameter optimization method for effluent TN prediction, the coefficient of determination ® increased by 0.092 and 0.067, reaching 0.725, and the root mean square error (RMSE) decreased by 0.262 and 0.215 mg/L, reaching 1.673 mg/L, respectively, after Bayesian optimization and data amplification.”
      原文链接:
    • NETL
    • NSTL

    New Findings from Technical University Dortmund (TU Dortmund) in Machine Learning Provides New Insights (Fitting Error Vs Parameter Performance-how To Choose Reliable Pc-saft Pure-component Parameters By Physics-informed Machine Learning)

    97-98页
    查看更多>>摘要：Current study results on Machine Learning have been published. According to news reporting from Dortmund, Germany, by NewsRx journalists, research stated, “State of the art thermodynamic models, such as the Perturbed-Chain Statistical Associating Fluid Theory (PC-SAFT), require a thorough parametrization (three pure-component parameters for nonassociating molecules) of the molecules considered. In our previous work (J. Habicht, C. Brandenbusch, G. Sadowski, Fluid Phase Equilibria, 2023, 565, 113657), we introduced a Machine Learning approach for a predictive parametrization of nonassociating components.” Funders for this research include German Research Foundation (DFG), German Research Foundation (DFG). The news correspondents obtained a quote from the research from Technical University Dortmund (TU Dortmund), “Within this approach, training is performed using a Huber-loss function, comparing the ML-predicted parameter set with the original one, e.g., from literature. However, often multiple purecomponent parameter sets exist for one molecule. This fact makes the training to only one 'true' parameter set questionable. Within this work, we thus performed a detailed analysis on the fact of multiparameter set existence. We further expanded our ML-approach by developing a choice of two physics-informed loss functions that allow for the consideration of multiple 'true' parameter sets during training. Results indicate that reliable pure-component parameters have a certain orientation when plotted in the three-dimensional parameter space.”
      原文链接:
    • NETL
    • NSTL
    • Amer Chemical Soc

    Pirogov Russian National Research Medical University Reports Findings in Machine Learning (Machine Learning-Based Decision- Making in Geriatrics: Aging Phenotype Calculator and Survival Prognosis)

    98-99页
    查看更多>>摘要：New research on Machine Learning is the subject of a report. According to news reporting originating from Moscow, Russia, by NewsRx correspondents, research stated, “Aging is a natural process with varying effects. As we grow older, our bodies become more susceptible to aging-associated diseases.” Our news editors obtained a quote from the research from Pirogov Russian National Research Medical University, “These diseases, individually or collectively, lead to the formation of distinct aging phenotypes. Identifying these aging phenotypes and understanding the complex interplay between coexistent diseases would facilitate more personalized patient management, a better prognosis, and a prolonged lifespan. Many studies distinguish between successful aging and frailty. However, this simple distinction fails to reflect the diversity of underlying causes. In this study, we sought to establish the underlying causes of frailty and determine the patterns in which these causes converge to form aging phenotypes. We conducted a comprehensive geriatric examination, cognitive assessment, and survival analysis of 2,688 long-living adults (median age = 92 years). The obtained data were clustered and used as input data for the Aging Phenotype Calculator, a multiclass classification model validated on an independent dataset of 96 older adults. The accuracy of the model was assessed using the receiver operating characteristic curve and the area under the curve. Additionally, we analyzed socioeconomic factors that could contribute to specific aging patterns. We identified five aging phenotypes: non-frailty, multimorbid frailty, metabolic frailty, cognitive frailty, and functional frailty. For each phenotype, we determined the underlying diseases and conditions and assessed the survival rate. Additionally, we provided management recommendations for each of the five phenotypes based on their distinct features and associated challenges. The identified aging phenotypes may facilitate better-informed decision-making.”
      原文链接:
    • NETL
    • NSTL

    Research Conducted at Khulna University of Engineering and Technology Has Updated Our Knowledge about Machine Learning (Assessment of Mechanical Properties With Machine Learning Modeling and Durability, and Microstructural Characteristics of a ...)

    99-100页
    查看更多>>摘要：A new study on Machine Learning is now available. According to news reporting originating in Khulna, Bangladesh, by NewsRx journalists, research stated, “Rising CO2 emissions have become one of the biggest environmental challenges in recent years. Due to the rising carbon footprint of the building industry, CO2 emission regulation and mitigation have become perennial issues.” Financial supporters for this research include Deputy for Research and Innovation - Ministry of Education, Kingdom of Saudi Arabia under the Institutional Funding Committee at Najran University, Kingdom of Saudi Arabia, Institutional Funding Committee at Najran University, Kingdom of Saudi Arabia. The news reporters obtained a quote from the research from the Khulna University of Engineering and Technology, “The utilization of Biochar (BC) as a carbon-sequestering component in cement mortar is the novelty and main concern of this study. In this research, the effectiveness of BC in sequestering carbon was examined along with its effect on the mechanical, microstructural, and durability characteristics of the composite cement mortar. It includes a control and eight additional mixes prepared with 1%, 3%, 5%, and 8% BC by weight of cement added to mortar; the BC were prepared at two fixed temperatures of 300 degrees C and 500 degrees C. It also involves testing fresh properties, mechanical properties, durability properties, and microstructure analysis using scanning electron microscopy (SEM) and energy-dispersive Xray analysis (EDX). A Universal Testing Machine (UTM) was used to determine the mechanical properties of the cement mortar, such as its compressive and tensile strengths. The water permeability and rapid chloride permeability tests (RCPT) were used to evaluate the specimens' long-term stability as a measure of their durability. Based on the test results, it has been found that the inclusion of BC enhanced the strength and durability of cement mortar through its pozzolanic action. In addition, BC is a filler material whose porous structure fills the voids within the cement particles, decreasing water absorption and improving workability. BC sequesters carbon by carbonizing biomass. Its presence in cement mortar stores carbon that would otherwise be emitted into the atmosphere, which helps to reduce the environmental effect. According to the conclusion of the study, BC has the potential to be a sustainable component of cement mortar. In addition, the two distinct algorithms built upon machine learning applied in the analysis using adaptive boosting (AdaBoost) and linear regression (LR); both these analyses demonstrate that it is feasible to predict the characteristics of cement mortar accurately.”
      原文链接:
    • NETL
    • NSTL
    • Elsevier

    Findings from West Virginia University in Artificial Intelligence Reported (Impact of accountability, training, and human factors on the use of artificial intelligence in healthcare: Exploring the perceptions of healthcare practitioners in the ...)

    100-101页
    查看更多>>摘要：Investigators discuss new findings in artificial intelligence. According to news reporting out of West Virginia University by NewsRx editors, research stated, “Effective integration, use, and adoption of Artificial Intelligence (AI) into the healthcare system will require human factors considerations and a systems approach in addition to predictive accuracy. This exploratory study focuses on clinicians' perception of the role of accountability, training and their impact on the intention of using AI and associated decision making.” The news reporters obtained a quote from the research from West Virginia University: “The study also captures the perception of clinicians on the role of AI on workload, trustworthiness, risk, and performance expectancy. A semi-structured survey, including itemized and open-ended questions, was distributed to healthcare practitioners working in the United States. Data were collected using an audience paneling company. A screening question exclusively selected healthcare professionals working actively within the United States of America. The study leveraged sequential regression and inductive content analysis to analyze quantitative and qualitative survey responses. Two hundred and sixty-five participants completed the survey. The findings showed a significant impact of various variables, including perceived workload, perceived trustworthiness of AI, perceived risk of AI, and willingness to receive AI training on using AI.”
      原文链接:
    • NETL
    • NSTL

    Findings on Robotics and Automation Detailed by Investigators at Carnegie Mellon University (anyloc: Towards Universal Visual Place Recognition)

    101-102页
    查看更多>>摘要：Investigators publish new report on Robotics - Robotics and Automation. According to news originating from Pittsburgh, Pennsylvania, by NewsRx correspondents, research stated, “Visual Place Recognition (VPR) is vital for robot localization. To date, the most performant VPR approaches are environment- and task-specific: while they exhibit strong performance in structured environments (predominantly urban driving), their performance degrades severely in unstructured environments, rendering most approaches brittle to robust real-world deployment.” Financial support for this research came from US Army Research Laboratory (ARL). Our news journalists obtained a quote from the research from Carnegie Mellon University, “In this work, we develop a universal solution to VPR - a technique that works across a broad range of structured and unstructured environments (urban, outdoors, indoors, aerial, underwater, and subterranean environments) without any re-training or finetuning. We demonstrate that general-purpose feature representations derived from off-the-shelf self-supervised models with no VPR-specific training are the right substrate upon which to build such a universal VPR solution. Combining these derived features with unsupervised feature aggregation enables our suite of methods, AnyLoc, to achieve up to 4x significantly higher performance than existing approaches. We further obtain a 6% improvement in performance by characterizing the semantic properties of these features, uncovering unique domains which encapsulate datasets from similar environments.”
      原文链接:
    • NETL
    • NSTL
    • IEEE

    University of Naples Federico Ⅱ Reports Findings in Diabetes Insipidus (Prediction of diabetes insipidus occurrence after endoscopic endonasal removal of sellar lesions using MRI-based radiomics and machine learning)

    102-103页
    查看更多>>摘要：New research on Nutritional and Metabolic Diseases and Conditions - Diabetes Insipidus is the subject of a report. According to news reporting from Naples, Italy, by NewsRx journalists, research stated, “Pituitary adenomas and craniopharyngiomas are the most common lesions of the sellar region. These tumors are responsible for invasion or compression of crucial neurovascular structures.” The news correspondents obtained a quote from the research from the University of Naples Federico II, “The involvement of the pituitary stalk warrants high rates of both pre- and post- operative diabetes insipidus. The aim of our study was to assess the accuracy of machine learning analysis from preoperative MRI of pituitary adenomas and craniopharyngiomas for the prediction of DI occurrence. All patients underwent MRI exams either on a 1.5- or 3-T MR scanner from two Institutions, including coronal T2- weighted (T2-w) and contrast-enhanced T1-weighted (CE T1-w) Turbo Spin Echo sequences. Feature selection was carried out as a multi-step process, with a threshold of 0.75 to identify robust features. Further feature selection steps included filtering based on feature variance (threshold >0.01) and pairwise correlation (threshold <0.80). A Bayesian Network model was trained with 10-fold cross validation employing SMOTE to balance classes exclusively within the training folds. Thirty patients were included in this study. In total 2394 features were extracted and 1791 (75%) resulted stable after ICC analysis. The number of variant features was 1351 and of non-colinear features was 125. Finally, 10 features were selected by oneR ranking. The Bayesian Network model showed an accuracy of 63% with a precision of 77% for DI prediction (0.68 area under the precision-recall curve).”
      原文链接:
    • NETL
    • NSTL

    University of California Davis Researcher Yields New Findings on Robotics (Trash Collection Gadget: A Multi-Purpose Design of Interactive and Portable Solution for Beach Cleanup)

    103-104页
    查看更多>>摘要：Research findings on robotics are discussed in a new report. According to news originating from the University of California Davis by NewsRx correspondents, research stated, “Beach pollution has become a pressing environmental concern with increased human activities. It calls for innovative solutions that can not only address the problem but also engage and educate individuals in the long run.” The news editors obtained a quote from the research from University of California Davis: “Herein, a prototype robotic cleanup system was introduced in this project in response to such a requirement. The project aims to design and develop a playful and user-friendly robotic system to accompany families and children during beach visits while actively gathering land rubbish or marine debris. The device is made up with three primary components, including a control system, a collection system, and a rubbish storage container. The control system receives operation instruction from cell phones through Bluetooth connection to direct the moving mode of the device. The collection system is equipped with plastic spikes and conveyor belts that can pick up and deposit trash into the storage container. The entire system is featured to be a portable 'beach buddy' that only takes up one cubic foot of space, allowing for easy transportation and operation.”
      原文链接:
    • NETL
    • NSTL