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    Study Findings on Machine Learning Are Outlined in Reports from University of California (Gait Event Detection and Travel Distance Using Waist-Worn Accelerometers across a Range of Speeds: Automated Approach)

    10-11页
    查看更多>>摘要：Current study results on artificial intelligence have been published. According to news originating from Davis, California, by NewsRx correspondents, research stated, “Estimation of temporospatial clinical features of gait (CFs), such as step count and length, step duration, step frequency, gait speed, and distance traveled, is an important component of community-based mobility evaluation using wearable accelerometers. However, accurate unsupervised computerized measurement of CFs of individuals with Duchenne muscular dystrophy (DMD) who have progressive loss of ambulatory mobility is difficult due to differences in patterns and magnitudes of acceleration across their range of attainable gait velocities.” Funders for this research include Us Department of Defense; Muscular Dystrophy Association; University of California Center For Information Technology Research in The Interest of Society (Citris) And The Banatao Institute. The news reporters obtained a quote from the research from University of California: “This paper proposes a novel calibration method. It aims to detect steps, estimate stride lengths, and determine travel distance. The approach involves a combination of clinical observation, machine-learning-based step detection, and regression-based stride length prediction. The method demonstrates high accuracy in children with DMD and typically developing controls (TDs) regardless of the participant’s level of ability. Fifteen children with DMD and fifteen TDs underwent supervised clinical testing across a range of gait speeds using 10 m or 25 m run/walk (10 MRW, 25 MRW), 100 m run/walk (100 MRW), 6-min walk (6 MWT), and free-walk (FW) evaluations while wearing a mobile-phone-based accelerometer at the waist near the body’s center of mass. Following calibration by a trained clinical evaluator, CFs were extracted from the accelerometer data using a multi-step machine-learning-based process and the results were compared to ground-truth observation data.”
      原文链接:
    • NETL
    • NSTL
    • Mdpi

    New Findings Reported from Harbin Institute of Technology Describe Advances in Robotics (A Novel Faster Fixed-time Adaptive Control for Robotic Systems With Input Saturation)

    11-12页
    查看更多>>摘要：Investigators publish new report on Robotics. According to news reporting originating in Harbin, People’s Republic of China, by NewsRx journalists, research stated, “In this article, an adaptive antisaturation fixedtime control method with a faster convergence rate is studied for uncertain robotic systems. First, a new segmental sliding variable is constructed to solve the singularity problem brought by the terminal sliding mode control (TSMC) and achieve a faster convergence rate.” Funders for this research include National Natural Science Foundation of China (NSFC), Natural Science Foundation of Heilongjiang Province, China Postdoctoral Science Foundation, Postdoctoral Initial Funding of Heilongjiang Province, Self-Planned Task of State Key Laboratory of Robotics and System (HIT). The news reporters obtained a quote from the research from the Harbin Institute of Technology, “Second, to approximate and compensate for the model uncertainty and the viscous friction parameter, an adaptive neural network (ANN) is employed. Then, a novel auxiliary system is constructed to mitigate the effects of input saturation. Based on this, a novel nonsingular TSMC algorithm integrated with the ANN and the auxiliary system is designed, so that the trajectory tracking errors of the robotic system can converge within a faster fixed time with actuator saturation.”
      原文链接:
    • NETL
    • NSTL
    • IEEE

    Studies from State University of New York (SUNY) Reveal New Findings on Machine Learning (A Review of Machine Learning Applications In Life Cycle Assessment Studies)

    12-13页
    查看更多>>摘要：Researchers detail new data in Machine Learning. According to news reporting originating from Albany, New York, by NewsRx correspondents, research stated, “Life Cycle Assessment (LCA) is a foundational method for quantitative assessment of sustainability. Increasing data availability and rapid development of machine learning (ML) approaches offer new opportunities to advance LCA.” Financial supporters for this research include USDA Agricultural Research Service, National Science Foundation (NSF), NIH National Institute on Aging (NIA), National Aeronautics & Space Administration (NASA). Our news editors obtained a quote from the research from the State University of New York (SUNY), “Here, we review current progress and knowledge gaps in applying ML techniques to support LCA, and identify future research directions for LCAs to better harness the power of ML. This review analyzes forty studies reporting quantitative assessment with a combination of LCA and ML methods. We found that ML approaches have been used for generating life cycle inventories, computing characterization factors, estimating life cycle impacts, and supporting life cycle interpretation. Most of the reviewed studies employed a single ML method, with artificial neural networks (ANNs) as the most frequently applied approach. Both supervised and unsupervised ML techniques were used in LCA studies. For studies using supervised ML, training datasets were derived from diverse sources, such as literature, lab experiments, existing databases, and model simulations. Over 70 % of these reviewed studies trained ML models with less than 1500 sample datasets. Although these reviewed studies showed that ML approaches help improve prediction accuracy, pattern discovery and computational efficiency, multiple areas deserve further research. First, continuous data collection and compilation is needed to support more reliable ML and LCA modeling. Second, future studies should report sufficient details regarding the selection criteria for ML models and present model uncertainty analysis. Third, incorporating deep learning models into LCA holds promise to further improve life cycle inventory and impact assessment.”
      原文链接:
    • NETL
    • NSTL
    • Elsevier

    Dongguan Maternal and Child Health Care Hospital Reports Findings in Cervical Cancer [Programmed cell death-index (PCDi) as a prognostic biomarker and predictor of drug sensitivity in cervical cancer: a machine learning-based analysis of mRNA ...]

    13-14页
    查看更多>>摘要：New research on Oncology - Cervical Cancer is the subject of a report. According to news reporting from Guangdong, People’s Republic of China, by NewsRx journalists, research stated, “: Cervical cancer is a significant public health concern, particularly in developing countries. Despite available treatment strategies, the prognosis for patients with locally advanced cervical cancer and beyond remains poor.” The news correspondents obtained a quote from the research from Dongguan Maternal and Child Health Care Hospital, “Therefore, an accurate prediction model that can reliably forecast prognosis is essential in clinical setting. Programmed cell death (PCD) mechanisms are diverse and play a critical role in tumor growth, survival, and metastasis, making PCD a potential reliable prognostic marker for cervical cancer. In this study, we created a novel prognostic indicator, programmed cell death-index (PCDi), based on a 10-fold cross-validation framework for comprehensive analysis of PCD-associated genes. Our PCDibased prognostic model outperformed previously published signature models, stratifying cervical cancer patients into two distinct groups with significant differences in overall survival prognosis, tumor immune features, and drug sensitivity. Higher PCDi scores were associated with poorer prognosis. The nomogram survival model integrated PCDi and clinical characteristics, demonstrating higher prognostic prediction performance. Furthermore, our study investigated the immune features of cervical cancer patients and found that those with high PCDi scores had lower infiltrating immune cells, lower potential of T cell dysfunction, and higher potential of T cell exclusion. Patients with high PCDi scores were resistant to classic chemotherapy regimens, including cisplatin, docetaxel, and paclitaxel, but showed sensitivity to the inhibitor SB505124 and Trametinib.”
      原文链接:
    • NETL
    • NSTL

    Universiti Malaya Reports Findings in Artificial Intelligence (A scoping review of applications of artificial intelligence in kinematics and kinetics of ankle sprains - current state-of-the-art and future prospects)

    14-15页
    查看更多>>摘要：New research on Artificial Intelligence is the subject of a report. According to news reporting originating from Kuala Lumpur, Malaysia, by NewsRx correspondents, research stated, “Despite the existence of evidence-based rehabilitation strategies that address biomechanical deficits, the persistence of recurrent ankle problems in 70% of patients with acute ankle sprains highlights the unresolved nature of this issue. Artificial intelligence (AI) emerges as a promising tool to identify definitive predictors for ankle sprains.” Our news editors obtained a quote from the research from Universiti Malaya, “This paper aims to summarize the use of AI in investigating the ankle biomechanics of healthy and subjects with ankle sprains. Articles published between 2010 and 2023 were searched from five electronic databases. 59 papers were included for analysis with regards to: i). types of motion tested (functional vs. purposeful ankle movement); ii) types of biomechanical parameters measured (kinetic vs kinematic); iii) types of sensor systems used (lab-based vs field-based); and, iv) AI techniques used. Most studies (83.1%) examined biomechanics during functional motion. Single kinematic parameter, specifically ankle range of motion, could obtain accuracy up to 100% in identifying injury status. Wearable sensor exhibited high reliability for use in both laboratory and on-field/clinical settings. AI algorithms primarily utilized electromyography and joint angle information as input data. Support vector machine was the most used supervised learning algorithm (18.64%), while artificial neural network demonstrated the highest accuracy in eight studies. The potential for remote patient monitoring is evident with the adoption of field-based devices. Nevertheless, AI-based sensors are underutilized in detecting ankle motions at risk of sprain.”
      原文链接:
    • NETL
    • NSTL

    Study Results from University Center Provide New Insights into Artificial Intelligence (Revolutionizing Corrugated Board Production and Optimization with Artificial Intelligence)

    15-15页
    查看更多>>摘要：Investigators publish new report on artificial intelligence. According to news reporting from the University Center by NewsRx journalists, research stated, “In the field of corrugated board production and packaging optimization, the advent of Artificial Intelligence (AI) has initiated a paradigm shift.” Our news reporters obtained a quote from the research from University Center: “This paper presents a brief analysis of AI’s role in revolutionizing both the production of corrugated board and the design of corrugated packaging. It explores the integration of AI in the homogenization process of complex corrugated board structures into single-layer, shallow shell-based computational models, aiming to improve and accelerate load-bearing calculations.” According to the news editors, the research concluded: “This work presents also how AI’s predictive and analytical capabilities are pivotal in achieving efficiency, sustainability, and cost-effectiveness in the corrugated board industry.”
      原文链接:
    • NETL
    • NSTL

    Findings from Indian Institute for Technology Provides New Data about Machine Learning (Machine Learning Assisted Construction of a Shallow Depth Dynamic Ansatz for Noisy Quantum Hardware)

    16-17页
    查看更多>>摘要：Investigators publish new report on Machine Learning. According to news reporting originating in Mumbai, India, by NewsRx journalists, research stated, “The development of various dynamic ansatz-constructing techniques has ushered in a new era, making the practical exploitation of Noisy Intermediate-Scale Quantum (NISQ) hardware for molecular simulations increasingly viable. However, such ansatz construction protocols incur substantial measurement costs during their execution.” Funders for this research include Industrial Research and Consultancy Centre, Council of Scientific & Industrial Research (CSIR) - India, Industrial Research and Consultancy Centre, IIT Bombay and Science and Engineering Research Board, Government of India. The news reporters obtained a quote from the research from Indian Institute for Technology, “This work involves the development of a novel protocol that capitalizes on regenerative machine learning methodologies and many-body perturbation theoretical measures to construct a highly expressive and shallow ansatz within the variational quantum eigensolver (VQE) framework with limited measurement costs. The regenerative machine learning model used in our work is trained with the basis vectors of a low-rank expansion of the N-electron Hilbert space to identify the dominant high-rank excited determinants without requiring a large number of quantum measurements. These selected excited determinants are iteratively incorporated within the ansatz through their low-rank decomposition. The reduction in the number of quantum measurements and ansatz depth manifests in the robustness of our method towards hardware noise, as demonstrated through numerical applications. Furthermore, the proposed method is highly compatible with state-of-the-art neural error mitigation techniques. This resource-efficient approach is quintessential for determining spectroscopic and other molecular properties, thereby facilitating the study of emerging chemical phenomena in the near-term quantum computing framework.”
      原文链接:
    • NETL
    • NSTL
    • Royal Soc Chemistry

    Reports Outline Robotics Research from University of South Florida (Consolidating Trees of Robotic Plans Generated Using Large Language Models to Improve Reliability)

    16-16页
    查看更多>>摘要：Current study results on robotics have been published. According to news originating from Tampa, Florida, by NewsRx correspondents, research stated, “The inherent probabilistic nature of Large Language Models (LLMs) introduces an element of unpredictability, raising concerns about potential discrepancies in their output.” Our news reporters obtained a quote from the research from University of South Florida: “This paper presents a novel approach designed to generate correct and optimal robotic task plans for diverse real-world demands and scenarios. LLMs have been used to generate task plans, but they are unreliable and may contain wrong, questionable, or high-cost steps. The proposed approach uses LLM to generate a number of task plans as trees and amalgamates them into a graph by removing questionable paths. Then an optimal task tree can be retrieved to circumvent questionable and high-cost nodes, thereby improving planning accuracy and execution efficiency. The approach is further improved by incorporating a large knowledge network.”
      原文链接:
    • NETL
    • NSTL

    University of Pittsburgh Reports Findings in Synostosis (Craniorate: an Image-based, Deep-phenotyping Analysis Toolset and Online Clinician Interface for Metopic Craniosynostosis)

    17-18页
    查看更多>>摘要：Current study results on Musculoskeletal Diseases and Conditions - Synostosis have been published. According to news reporting out of Pittsburg, California, by NewsRx editors, research stated, “The diagnosis and management of metopic craniosynostosis involve subjective decision-making at the point of care. The purpose of this work was to describe a quantitative severity metric and point-of-care user interface to aid clinicians in the management of metopic craniosynostosis and to provide a platform for future research through deep phenotyping.”
      原文链接:
    • NETL
    • NSTL
    • Lippincott Williams & Wilkins

    State University of New York (SUNY) Buffalo Reports Findings in Premature Birth (Improving Prediction of Survival for Extremely Premature Infants Born at 23 to 29 Weeks Gestational Age in the Neonatal Intensive Care Unit: Development and ...)

    19-20页
    查看更多>>摘要：New research on Pregnancy Complications - Premature Birth is the subject of a report. According to news originating from Buffalo, New York, by NewsRx correspondents, research stated, “Infants born at extremely preterm gestational ages are typically admitted to the neonatal intensive care unit (NICU) after initial resuscitation. The subsequent hospital course can be highly variable, and despite counseling aided by available risk calculators, there are significant challenges with shared decision-making regarding life support and transition to end-of-life care.” Our news journalists obtained a quote from the research from the State University of New York (SUNY) Buffalo, “Improving predictive models can help providers and families navigate these unique challenges. Machine learning methods have previously demonstrated added predictive value for determining intensive care unit outcomes, and their use allows consideration of a greater number of factors that potentially influence newborn outcomes, such as maternal characteristics. Machine learning-based models were analyzed for their ability to predict the survival of extremely preterm neonates at initial admission. Maternal and newborn information was extracted from the health records of infants born between 23 and 29 weeks of gestation in the Medical Information Mart for Intensive Care Ⅲ (MIMIC-III) critical care database. Applicable machine learning models predicting survival during the initial NICU admission were developed and compared. The same type of model was also examined using only features that would be available prepartum for the purpose of survival prediction prior to an anticipated preterm birth. Features most correlated with the predicted outcome were determined when possible for each model. Of included patients, 37 of 459 (8.1%) expired. The resulting random forest model showed higher predictive performance than the frequently used Score for Neonatal Acute Physiology With Perinatal Extension Ⅱ (SNAPPE-II) NICU model when considering extremely preterm infants of very low birth weight. Several other machine learning models were found to have good performance but did not show a statistically significant difference from previously available models in this study. Feature importance varied by model, and those of greater importance included gestational age; birth weight; initial oxygenation level; elements of the APGAR (appearance, pulse, grimace, activity, and respiration) score; and amount of blood pressure support. Important prepartum features also included maternal age, steroid administration, and the presence of pregnancy complications. Machine learning methods have the potential to provide robust prediction of survival in the context of extremely preterm births and allow for consideration of additional factors such as maternal clinical and socioeconomic information.”
      原文链接:
    • NETL
    • NSTL
    • Jmir Publications, Inc