查看更多>>摘要：New study results on artificial intelligence have been published. According to news reporting originating from Madhyanchal Professional University by NewsRx correspondents, research stated, “The increasing prevalence of machine learning technology highlights the urgent need to delve into its insinuations for safety and confidentiality. While inquiry on the safety aspects of mechanism knowledge has garnered considerable attention, privacy considerations have often taken a backseat, although recent years have seen a significant upswing in privacy-focused research.” Our news editors obtained a quote from the research from Madhyanchal Professional University: “In an effort to contribute to this growing field, we conducted an analysis encompassing more than 40 articles addressing privacy threats in the context of mechanism knowledge, published ended the historical seven centuries. We have contributed to this research by creating a thorough threat architecture and an assault taxonomy. These tools help in categorizing various attacks based on the assets they target and the knowledge adversaries possess. We also conducted an in-depth exploration of the different privacy threats posed by machine learning, shedding light on their mechanisms and implications. Furthermore, our research includes a preliminary investigation into the underlying reasons for privacy breaches in machine learning systems. This aspect delves into the root causes of privacy leaks, shedding light on the factors that make such incidents more likely to occur. In addition to identifying privacy threats and their causes, we have compiled a summary of the most commonly proposed defense mechanisms against these threats. These defences can serve as a resource for organizations and researchers seeking to bolster the privacy of their machine learning systems. Lastly, we recognize that the field of machine learning privacy still faces unanswered questions and developing difficulties.”
查看更多>>摘要：Data detailed on Robotics have been presented. According to news reporting originating from Harbin, People's Republic of China, by NewsRx correspondents, research stated, “Existing works in the optimization of five-axis machining mainly focus on the efficiency, precision, or dynamic performance of the machine tools, while the performance of other equipment which assists the machining process has not been considered. This paper takes the robot assisted supporting machining of thin-walled blades as the research objective and proposes an axial-sensitivity-reduction based five-axis toolpath re-scheduling method for facilitating the collaborative support machining with a machine-tool and a robot.” Financial support for this research came from National Natural Science Foundation of China (NSFC). Our news editors obtained a quote from the research from Harbin Engineering University, “The input of the method is lenient, merely including the original toolpath and the workpiece point-cloud model. The output of the method is the re-scheduled toolpath which requires lower motion ability of the assisted support equipment. This is realized by the following approaches. First, an axial sensitivity concept is defined, which quantificationally reflects the influence extent of the machine-tool axis motion on the assisted supporter motion, thus the most sensitive axis which affects the assisted process maximally can be identified. Then, an optimal-partition dual-domain-assisted-fitting method is provided to reconstruct the parameterized geometrical model of blades according to the point-cloud model, thus the multi-value property of the blades which troubles the surface construction is solved. After that, a sensitive-axis-calming bidirectional-tangent-bug-searching method is proposed to re-schedule the toolpath of five-axis machining, thus reducing the sensitivity of the most-sensitive axis. The whole method is universal, because a general cutter model is used and the cutter-contact point keeps invariant after re-scheduling.”
查看更多>>摘要：New research on Machine Learning is the subject of a report. According to news reporting originating in Groningen, Netherlands, by NewsRx journalists, research stated, “Infection-related consultations on intensive care units (ICU) have a positive impact on quality of care and clinical outcome. However, timing of these consultations is essential and to date they are typically event-triggered and reactive.” Financial support for this research came from European Commission Horizon 2020 Framework Marie Sklodowska-Curie Actions. The news reporters obtained a quote from the research from University Medical Center Groningen, “Here, we investigate a proactive approach to identify patients in need for infection-related consultations by machine learning models using routine electronic health records. Data was retrieved from a mixed ICU at a large academic tertiary care hospital including 9684 admissions. Infection-related consultations were predicted using logistic regression, random forest, gradient boosting machines, and long short-term memory neural networks (LSTM). Overall, 7.8% of admitted patients received an infection-related consultation. Time-sensitive modelling approaches performed better than static approaches. Using LSTM resulted in the prediction of infection-related consultations in the next clinical shift (up to eight hours in advance) with an area under the receiver operating curve (AUROC) of 0.921 and an area under the precision recall curve (AUPRC) of 0.541. The successful prediction of infection-related consultations for ICU patients was done without the use of classical triggers, such as (interim) microbiology reports.”
查看更多>>摘要：A new study on artificial intelligence is now available. According to news reporting originating from Julich, Germany, by NewsRx correspondents, research stated, “A reliable supply with electric power is vital for our society.” Funders for this research include Bundesministerium Fur Bildung Und Forschung; Helmholtz Association. Our news correspondents obtained a quote from the research from Institute for Energy: “Transmission line failures are among the biggest threats for power grid stability as they may lead to a splitting of the grid into mutual asynchronous fragments. New conceptual methods are needed to assess system stability that complement existing simulation models. In this article, we propose a combination of network science metrics and machine learning models to predict the risk of desynchronization events. Network science provides metrics for essential properties of transmission lines such as their redundancy or centrality. Machine learning models perform inherent feature selection and, thus, reveal key factors that determine network robustness and vulnerability. As a case study, we train and test such models on simulated data from several synthetic test grids.”
查看更多>>摘要：New research on Machine Learning is the subject of a report. According to news originating from Nijmegen, Netherlands, by NewsRx correspondents, research stated, “Using neurophysiological measures to model how the brain performs complex cognitive tasks such as mental rotation is a promising way towards precise predictions of behavioural responses. The mental rotation task requires objects to be mentally rotated in space.” Financial support for this research came from Fonds National de la Recherche Luxembourg. Our news journalists obtained a quote from the research from Radboud University, “It has been used to monitor progressive neurological disorders. Up until now, research on neural correlates of mental rotation have largely focused on group analyses yielding models with features common across individuals. Here, we propose an individually tailored machine learning approach to identify person-specific patterns of neural activity during mental rotation. We trained ridge regressions to predict the reaction time of correct responses in a mental rotation task using task-related, electroencephalographic (EEG) activity of the same person. When tested on independent data of the same person, the regression model predicted the reaction times significantly more accurately than when only the average reaction time was used for prediction (bootstrap mean difference of 0.02, 95% CI: 0.01-0.03, p<.001). When tested on another person's data, the predictions were significantly less accurate compared to within-person predictions. Further analyses revealed that considering person-specific reaction times and topographical activity patterns substantially improved a model's generalizability.”
查看更多>>摘要：New study results on robotics have been published. According to news originating from China Jiliang University by NewsRx editors, the research stated, “The international standard ISO 9283:1998 is popular for performance tests of industrial robots at present.” Financial supporters for this research include National Natural Science Foundation of China. The news journalists obtained a quote from the research from China Jiliang University: “It is desirable that the tests described in this standard should be sensitive to error sources of robot end positioning/ orientation. In this paper, firstly, the kinematic and the joint stiffness model parameters are identified experimentally for two models of 6-DOF (Degree of Freedom) serial in-dustrial robots (i.e., the ABB IRB 1410 and UR5 robots). Then, the standard deviations of the de-rived model parameters are obtained as error inputs for the sensitivity analysis of the performance tests including the positioning/orientation accuracy/repeatability tests. By simulating the error sen-sitivity of the positioning/orientation accuracy/ repeatability test methods for industrial robots, it is analyzed whether the tests described in the ISO 9283:1998 Standard are sensitive to the focused er-ror sources, showing the limitations of the evaluation index of the ISO 9283:1998 Standard.. The results show that for 6-DOF serial industrial robots, the positioning accuracy test is the key to de-termining their motion performance. The orientation accuracy and repeatability tests are not neces-sary if the positioning accuracy and repeatability tests can be done for 6-DOF serial industrial robots.”
查看更多>>摘要：New research on Robotics is the subject of a report. According to news reporting from Oxford, United Kingdom, by NewsRx journalists, research stated, “Communication in surgery is integral to the fundamentals of perioperative nursing practice and patient safety. Research exploring team communication in robotic-assisted surgery (RAS) is evident in the literature but little attention has been focused on how the experiences of operating room nurses' communication affect safety, practice and patient care outcomes.” The news correspondents obtained a quote from the research from Oxford Brookes University, “To synthesise current evidence regarding communication during robotic-assisted surgery as experienced by registered nurses. An integrative literature review informed by Whittemore and Knafl's (2005) methodology was used to conduct a rigorous analysis and synthesis of evidence. A comprehensive database search was conducted using PRISMA guidelines. CINAHL, Pubmed, PsychINFO and British Nursing Web of Science databases were searched using a Boolean strategy. Twenty-five relevant papers were included in this literature review. Thematic analysis revealed two main themes with four related subthemes. The two main themes are: 'Adaptive operating room nursing in RAS' and 'RAS alters team dynamics'. The four subthemes are: 'Navigating disruptions in RAS', 'RAS heightens interdependence on team working', 'Augmented communicative workflow in RAS', and 'Professional empowerment to speak up'. This integrative review identifies how current research largely focuses on communication in the wider OR team. However, current evidence lacks the input of nurses. Therefore, further evidence is needed to explore nurses' experiences to highlight their perspectives. Robotics significantly benefit patients, and this review identifies different challenges that robotic-assisted surgery nurses encounter.”
查看更多>>摘要：New research on Artificial Intelligence is the subject of a report. According to news reporting out of Beijing, People's Republic of China, by NewsRx editors, research stated, “The mechanical properties serve as crucial quality indicators for cold-rolled strips. For a long time, the mechanical properties mechanism and data-driven models can't comprehensively consider sufficient factors to achieve high-accuracy prediction due to the 'data-isolated island' between production lines.” Our news journalists obtained a quote from the research from the University of Science and Technology Beijing, “In this research, we introduce a multi-process collaborative platform based on the industrial internet system. This platform is designed to enable real-time collection of diverse and heterogeneous data from both upstream and downstream processes of cold rolling. On this basis, a novel mechanical properties interval prediction model is proposed using the sparrow search algorithm to optimize fast learning network under the LUBE framework. We trained the model by using a dataset collected from a large steel plant. Based on the rolling theory and Pearson correlation coefficient, 25 features are selected as the inputs of the prediction model.”
查看更多>>摘要：New research on Robotics - Robotics and Automation is the subject of a report. According to news originating from Stanford, California, by NewsRx correspondents, research stated, “Trajectory optimization under uncertainty underpins a wide range of applications in robotics. However, existing methods are limited in terms of reasoning about sources of epistemic and aleatoric uncertainty, space and time correlations, nonlinear dynamics, and non-convex constraints.” Financial support for this research came from NASA University Leadership Initiative. Our news journalists obtained a quote from the research from Stanford University, “In this work, we first introduce a continuous-time planning formulation with an average-value-at-risk constraint over the entire planning horizon. Then, we propose a sample-based approximation that unlocks an efficient and generalpurpose algorithm for risk-averse trajectory optimization. We prove that the method is asymptotically optimal and derive finite-sample error bounds.” According to the news editors, the research concluded: “Simulations demonstrate the high speed and reliability of the approach on problems with stochasticity in nonlinear dynamics, obstacle fields, interactions, and terrain parameters.”
查看更多>>摘要：Current study results on artificial intelligence have been published. According to news originating from Can Tho, Vietnam, by NewsRx editors, the research stated, “Living standards are rising due to a more developed society, and recreation, particularly tourism, is becoming more critical. Expanding the tourist industry is one of the most significant concerns in economic growth.” Our news correspondents obtained a quote from the research from Can Tho University of Medicine and Pharmacy: “Tourism revenue has helped increase residents' income, leading to socio-economic development. In recent years, emerging Vietnamese tourism spots like Hon Son, Sapa, Hue, Phu Quoc in Vietnam, and others have consistently drawn travellers to visit and experience through social networking platforms. Tourism potential is tremendous, but foreign visitors' information about tourist destinations still needs to be improved. This work proposes an approach to integrating machine learning algorithms into an information system to consult tourism traveling. Machine learning algorithms can classify question topics, predict user intent, and predict conversation scenarios to give appropriate responses. Our method is evaluated on the dataset, including 7319 samples on 11 topics collected from the TWCS dataset, using three algorithms: Bag of Words, BERT, and RoBERTa. BERT achieved the highest performance among the surveyed algorithms with 90 % in accuracy and 90.1 % in F1-Score. From the trained model, the team built a mobile application on Android to deploy the chatbot application with the Flutter framework based on Dart, an object-oriented programming language developed by Google using the concept of containers. The system's functionality serves two primary user groups: administrators and application users.”
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