查看更多>>摘要：Researchers detail new data in Artificial Intelligence. According to news originating from Durham, North Carolina, by NewsRx editors, the research stated, “Implementing Artificial Intelligence in medicine is revolutionizing how medicine is practiced. It has much promise in bringing about improved clinical outcomes and efficiency while decreasing costs.” Financial support for this research came from NIH National Cancer Institute (NCI). Our news journalists obtained a quote from the research from Duke University, “There are also concerns and unintended consequences that are being realized and significant efforts to consider ethical principles in the implementation of Artificial Intelligence in medicine. One potential consequence may be the loss of what has been described as the soul of medicine: the physician-patient relationship. This relationship is especially precious in the context of what the US Surgeon General Vivek H. Murthy MD has called an ‘Epidemic of Loneliness and Isolation.’ This commentary describes considerations and potential steps to protect this vital relationship while implementing Artificial Intelligence approaches to improving patient care.”
查看更多>>摘要：Investigators publish new report on Robotics. According to news reporting out of Wuhan, People’s Republic of China, by NewsRx editors, research stated, “Collaborative robots (Cobots) are essential in flexible automation solutions, enabling fast and easy reconfiguration to adapt to varying task requirements in dynamic environments. This requires the ability to safely handle different payloads with varying inertial parameters, which may not be known in advance.” Financial support for this research came from National Natural Science Foundation of China (NSFC). Our news journalists obtained a quote from the research from the Huazhong University of Science and Technology, “Hence, online identification of the payload’s inertial parameters becomes essential for safe interactions, accurate path following, and stable grasping. Most existing methods require additional sensors, calibration procedures, or custom filtering, which increases the complexity and estimation time. In this letter, we propose a novel online identification method that employs a bagging ensemble machine learning approach to identify the payload inertial parameters without external sensors or additional filtering and calibration steps. The method uses available joint position, velocity, and torque measurements from the Cobot to train neural networks and decision trees as weak learners. The method is tested in simulation and validated using the Franka Emika Panda Cobot.”
查看更多>>摘要：New research on Machine Learning is the subject of a report. According to news reporting originating from Guangxi, People’s Republic of China, by NewsRx correspondents, research stated, “To explore the value of CT-based radiomics model in the differential diagnosis of benign ovarian tumors (BeOTs), borderline ovarian tumors (BOTs), and early malignant ovarian tumors (eMOTs). The retrospective research was conducted with pathologically confirmed 258 ovarian tumor patients from January 2014 to February 2021.” Our news editors obtained a quote from the research from the Guilin University of Aerospace Technology, “The patients were randomly allocated to a training cohort (n = 198) and a test cohort (n = 60). By providing a three-dimensional (3D) characterization of the volume of interest (VOI) at the maximum level of images, 4238 radiomic features were extracted from the VOI per patient. The Wilcoxon-Mann-Whitney (WMW) test, least absolute shrinkage and selection operator (LASSO), and support vector machine (SVM) were employed to select the radiomic features. Five machine learning (ML) algorithms were applied to construct three-class diagnostic models. Leave-one-out cross-validation (LOOCV) was implemented to evaluate the performance of the radiomics models. The test cohort was used to verify the generalization ability of the radiomics models. The receiver-operating characteristic (ROC) was used to evaluate diagnostic performance of radiomics model. Global and discrimination performance of five models was evaluated by average area under the ROC curve (AUC). The average ROC indicated that random forest (RF) diagnostic model in training cohort demonstrated the best diagnostic performance (micro/macro average AUC, 0.98/0.99), which was then confirmed with by LOOCV (micro/macro average AUC, 0.89/0.88) and external validation (test cohort) (micro/macro average AUC, 0.81/0.79).”
查看更多>>摘要：Researchers detail new data in artificial intelligence. According to news reporting originating from Vila Real, Portugal, by NewsRx correspondents, research stated, “The modeling of metal concentrations in large rivers is complex because the contributing factors are numerous, namely, the variation in metal sources across spatiotemporal domains.” The news correspondents obtained a quote from the research from Universidade de Tras-os-Montes e Alto Douro: “By considering both domains, this study modeled metal concentrations derived from the interaction of river water and sediments of contrasting grain size and chemical composition, in regions of contrasting seasonal precipitation. Statistical methods assessed the processes of metal partitioning and transport, while artificial intelligence methods structured the dataset to predict the evolution of metal concentrations as a function of environmental changes. The methodology was applied to the Paraopeba River (Brazil), divided into sectors of coarse aluminum-rich natural sediments and sectors enriched in fine iron- and manganese-rich mine tailings, after the collapse of the B1 dam in Brumadinho, with 85-90% rainfall occurring from October to March.”
查看更多>>摘要：New research on Machine Learning is the subject of a report. According to news reporting originating from Goteborg, Sweden, by NewsRx correspondents, research stated, “Altering chemical reactivity and material structure in confined optical environments is on the rise, and yet, a conclusive understanding of the microscopic mechanisms remains elusive. This originates mostly from the fact that accurately predicting vibrational and reactive dynamics for soluted ensembles of realistic molecules is no small endeavor, and adding (collective) strong light-matter interaction does not simplify matters.” Our news editors obtained a quote from the research from the Chalmers University of Technology, “Here, we establish a framework based on a combination of machine learning (ML) models, trained using density-functional theory calculations and molecular dynamics to accelerate such simulations. We then apply this approach to evaluate strong coupling, changes in reaction rate constant, and their influence on enthalpy and entropy for the deprotection reaction of 1-phenyl-2-trimethylsilylacetylene, which has been studied previously both experimentally and using simulations. While we find qualitative agreement with critical experimental observations, especially with regard to the changes in kinetics, we also find differences in comparison with previous theoretical predictions. The features for which the ML-accelerated and simulations agree show the experimentally estimated kinetic behavior. Conflicting features indicate that a contribution of dynamic electronic polarization to the reaction process is more relevant than currently believed.”
查看更多>>摘要：Data detailed on artificial intelligence have been presented. According to news originating from Bucharest, Romania, by NewsRx correspondents, research stated, “The study explores key academic competencies and professional skills in data science in the context of the development of artificial intelligence, highlighting their importance in the business environment.” The news correspondents obtained a quote from the research from Bucharest University of Economic Studies: “Using the ‘2022 Stack Overflow Annual Developer Survey’ dataset and machine learning methods such as principal component analysis, K-means clustering, and logistic regression, professional skills in science are analysed the data. The research targets the distribution of jobs in the field, the level of experience, the languages and analysis programs used, the support offered by companies, and the dynamics of data science teams, as well as the impact that artificial intelligence has on the field. With their help, a comprehensive understanding of the impact of academic training on career opportunities in the field of data science is provided, contributing to the development of the profile of the qualified specialist in this field. The research also provides relevant pointers and recommendations for enhancing the skills required in data science in order to outline a skilled profile and fulfil the demands of the business environment in a world dominated by data analytics and artificial intelligence. By including academic skills in the process of training data science specialists, the research brings innovation and highlights the skills needed to be trained in the academic field to facilitate the employment of graduates in specific fields of data science.”
查看更多>>摘要：New research on Immune System Diseases and Conditions - HIV/AIDS is the subject of a report. According to news reporting out of Jiangxi, People’s Republic of China, by NewsRx editors, research stated, “Secondary immunodeficiency can arise from various clinical conditions that include HIV infection, chronic diseases, malignancy and long-term use of immunosuppressives, which makes the suffering patients susceptible to all types of pathogenic infections. Other than HIV infection, the possible pathogen profiles in other aetiology-induced secondary immunodeficiency are largely unknown.” Our news journalists obtained a quote from the research from the Jiangxi University of Finance and Economics, “Medical records of the patients with secondary immunodeficiency caused by various aetiologies were collected from the First Affiliated Hospital of Nanchang University, China. Based on these records, models were developed with the machine learning method to predict the potential infectious pathogens that may inflict the patients with secondary immunodeficiency caused by various disease conditions other than HIV infection. Several metrics were used to evaluate the models’ performance. A consistent conclusion can be drawn from all the metrics that Gradient Boosting Machine had the best performance with the highest accuracy at 91.01%, exceeding other models by 13.48, 7.14, and 4.49% respectively.”
查看更多>>摘要：New study results on robotics have been published. According to news originating from Pulau Pinang, Malaysia, by NewsRx editors, the research stated, “Over recent decades, the field of mobile robot path planning has evolved significantly, driven by the pursuit of enhanced navigation solutions. The need to determine optimal trajectories within complex environments has led to the exploration of diverse path planning methodologies.” Financial supporters for this research include Collaborative Research in Engineering, Science, And Technology. Our news reporters obtained a quote from the research from University Sains Malaysia: “This paper focuses on a specific subset: Bio-inspired Population-based Optimization (BPO) methodologies. BPO methods play a pivotal role in generating efficient paths for path planning. Amidst the abundance of optimization approaches over the past decade, only a fraction of studies has effectively integrated these methods into path planning strategies. This paper’s focus is on the years 2014-2023, reviewing BPO techniques applied to mobile robot path planning challenges. Contributions include a comprehensive review of recent BPO methods in mobile robot path planning, along with an experimental methodology to compare them under consistent conditions. This encompasses the same environment, initial conditions, and replicates. A multi-objective function is incorporated to evaluate optimization methods. The paper delves into key concepts, mathematical models, and algorithm implementations of examined optimization techniques. The experimental setup, methodology, and benchmarking performance results are discussed. Based on the proposed experimental methodology, Improved Sparrow Search Algorithm (ISpSA) shows the best cost improvement percentage (7.87%), but suffers in terms of optimization time.”
查看更多>>摘要：Research findings on Robotics are discussed in a new report. According to news reporting from Guangzhou, People’s Republic of China, by NewsRx journalists, research stated, “Folding a flat sheet under a specific crease pattern can form a three-dimensional origami tube, which has been proven to exhibit unique mechanical properties and has wide engineering applications. In this study, a novel Miura-derived origami tube is designed, and its precise circular closing condition and mechanical properties are systematically analyzed, revealing that the proposed origami tube has programmable stiffness characteristics.” Financial support for this research came from China Scholarship Council. The news correspondents obtained a quote from the research from Guangzhou University, “Polyvinyl chloride (PVC) sheet enables the origami tube to have the characteristics of flexibility, bending, compression, and torsion resistance. Then, a modular design strategy for the novel continuous robot constructed with the Miura-derived origami tube as the backbone is proposed. A continuous robot with three origami tube modules in series is designed and fabricated as a representative case. Three steel wires drive each module to achieve independent contraction or bending movement. The unified installation of steel wire-driven motors on the base endows the robot with a lightweight, interconnected inner space, high scalability, and flexibility backbone. The kinematic relationships among the driving space, configuration space, and task space are investigated by geometric model and constant curvature assumption, with the circular trajectory tracking and reachable workspace revealed by numerical simulations. Further, the kinematic decoupling or the multi-driving model based on the stiffness difference of the origami tube between multi-segments is discussed.”
查看更多>>摘要：Researchers detail new data in Machine Learning. According to news reporting originating in Wuxi, People’s Republic of China, by NewsRx journalists, research stated, “Microbial community is a key contributing factor for flavor formation in natural food fermentation. However, it is a challenge to maintain batch -to -batch uniformity during the fermentation process due to the diversity and variability of microbial community.” Financial supporters for this research include National Key Research and Devel- opment Program of China, National Natural Science Foundation of China (NSFC), International Science and Technology Cooperation Research Program of Zhenjiang, Jiangsu Provincial project. The news reporters obtained a quote from the research from Jiangnan University, “A rapid detection of the structure and function of the microbial community in the whole fermentation process is of great importance for quality control of the final fermentation products. Firstly, we employed amplicon sequencing to target the dominant operational taxonomic units in the microbial community of Zhenjiang aromatic vinegar, a traditional cereal vinegar. Secondly, we isolated and created a Raman database for 13 dominant bacterial species from vinegar culture, enabling us to establish a logistic regression model with 96.4% accuracy in species classification. Finally, a Raman -fermentation phase regression model was established, achieving an R2 of 0.952, accurately determining the actual fermentation phase of vinegar.”
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