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    Researchers’ Work from Harbin Institute of Technology Focuses on Artificial Intelligence (Emotional Expression By Artificial Intelligence Chatbots To Improve Customer Satisfaction: Underlying Mechanism and Boundary Conditions)

    59-59页
    查看更多>>摘要：Current study results on Artificial Intelligence have been published. According to news reporting originating in Heilongjiang, People’s Republic of China, by NewsRx journalists, research stated, “Artificial intelligence chatbots have invaded the tourism industry owing to their low cost and high efficiency. However, the influence of emotional expressions of chatbots on service outcomes has not received much attention from researchers.” Funders for this research include National Natural Science Foundation of China (NSFC), Fundamental Research Funds for the Central Universities in UIBE, Fundamental Research Funds in DUT, Natural Science Foundation of Heilongjiang Province, Fundamental Research Funds for the Central Universities. The news reporters obtained a quote from the research from the Harbin Institute of Technology, “Drawing upon expectancy violations theory, we explored how emotional expressions of chatbots affect customer satisfaction using three experiments in the context of tourist attraction recommendations. Chatbots’ expressions of concern for customers can improve customer satisfaction by reducing expectancy violations. In particular, customer’s goal orientation, the human-likeness of chatbot’s avatars, and the relationship type between customers and chatbots can moderate the negative relationship between emotional expression and expectancy violation.”
      原文链接:
    • NETL
    • NSTL
    • Elsevier

    New Robotics Study Findings Have Been Reported by Investigators at Jiangsu University (Deformation and Locomotion of Untethered Small-scale Magnetic Soft Robotic Turtle With Programmable Magnetization)

    60-60页
    查看更多>>摘要：A new study on Robotics is now available. According to news reporting out of Zhenjiang, People’s Republic of China, by NewsRx editors, research stated, “Inspired by the way sea turtles rely on the Earth’s magnetic field for navigation and locomotion, a novel magnetic soft robotic turtle with programmable magnetization has been developed and investigated to achieve biomimetic locomotion patterns such as straight-line swimming and turning swimming. The soft robotic turtle (12.50 mm in length and 0.24 g in weight) is integrated with an Ecoflex-based torso and four magnetically programmed acrylic elastomer VHB-based limbs containing samarium-iron-nitrogen particles, and was able to carry a load more than twice its own weight.” Financial supporters for this research include National Natural Science Foundation of China (NSFC), National Natural Science Foundation of China (NSFC), Natural Science Foundation of Jiangsu Province, Research Project of State Key Laboratory of Mechanical System and Vibration, National Natural Science Foundation of China (NSFC), Opening project of the Key Laboratory of Bionic Engineering (Ministry of Education), Jilin University.
      原文链接:
    • NETL
    • NSTL
    • Springer Nature

    Findings from Shanghai University in the Area of Robotics Described (Formation Tracking of Multi-robot Systems With Switching Directed Topologies Based On Udwadia-kalaba Approach)

    61-61页
    查看更多>>摘要：Researchers detail new data in Robotics. According to news originating from Shanghai, People’s Republic of China, by NewsRx correspondents, research stated, “In this paper, a distributed formation tracking protocol is proposed for multi-robot systems with switching directed topologies based on the Udwadia-Kalaba approach.” Financial supporters for this research include National Natural Science Foundation of China (NSFC), Doctoral Scientific Research Staring Foundation of Chongqing Normal University, Science and Technology Research Program of Chongqing Municipal Education Commission, Innovation Program of Shanghai Municipal Education Commission. Our news journalists obtained a quote from the research from Shanghai University, “The basic idea is to use the consensus-based scheme to reconstruct formation tracking control requirement into a second order constraint first, and then apply the Udwadia-Kalaba approach to obtain the constraint force required to achieve the formation tracking, and afterward derive the explicit equations of motion for the constrained multi-robot systems for the control algorithm design. The convergence analysis of the tracking error system is conducted to affirm that the proposed control algorithm can achieve the formation tracking when the switching directed topologies have a directed spanning tree across each switching time interval.”
      原文链接:
    • NETL
    • NSTL
    • Elsevier

    Findings on Machine Learning Reported by Investigators at Indian Institute of Technology (High Resolution Landslide Susceptibility Mapping Using Ensemble Machine Learning and Geospatial Big Data)

    62-63页
    查看更多>>摘要：Data detailed on Machine Learning have been presented. According to news reporting originating in New Delhi, India, by NewsRx journalists, research stated, “Landslide susceptibility represents the potential of slope failure for given geo-environmental conditions. The existing landslide susceptibility maps suffer from several limitations, such as being based on limited data, heuristic methodologies, low spatial resolution, and small areas of interest.” Funders for this research include ISRO Space Applications Center, DST IC-IMPACTS, IIT Delhi IoE. The news reporters obtained a quote from the research from the Indian Institute of Technology, “In this study, we overcome all these limitations by developing a probabilistic framework that combines imbalance handling and ensemble machine learning for landslide susceptibility mapping. We employ a combination of One -Sided Selection and Support Vector Machine Synthetic Minority Oversampling Technique (SVMSMOTE) to eliminate class imbalance and develop smaller representative data from big data for model training. A blending ensemble approach using hyperparameter tuned Artificial Neural Networks, Random Forests, and Support Vector Machine, is employed to reduce the uncertainty associated with a single model. The methodology provides the landslide susceptibility probability and a landslide susceptibility class. A thorough evaluation of the framework is performed using receiver operating characteristic curves, confusion matrices, and the derivatives of confusion matrices. This framework is used to develop India’s first national-scale machine learning based landslide susceptibility map. The landslide database is carefully curated from global and local inventories, and the landslide conditioning factors are selected from a multitude of geophysical and climatological variables. The Indian Landslide Susceptibility Map (ILSM) is developed at a resolution of 0.001 degrees (similar to 100 m) and is classified into five classes: very low, low, medium, high, and very high. We report an accuracy of 95.73 %, sensitivity of 97.08 %, and matthews correlation coefficient (MCC) of 0.915 on test data, demonstrating the accuracy, robustness, and generalizability of the framework for landslide identification. The model classified 4.75 % area in India as very highly susceptible to landslides and detected new landslide susceptible zones in the Eastern Ghats, hitherto unreported in the government landslide records.”
      原文链接:
    • NETL
    • NSTL
    • Elsevier

    Study Findings on Machine Learning Are Outlined in Reports from University of Brasilia (Experimental vibration dataset collected of a beam reinforced with masses under different health conditions)

    62-62页
    查看更多>>摘要：New research on artificial intelligence is the subject of a new report. According to news reporting from Brasilia, Brazil, by NewsRx journalists, research stated, “Vibration signals extracted from structures across diverse health conditions have become indispensable for monitoring structural integrity.” Funders for this research include Coordenacao De Aperfeicoamento De Pessoal De Nivel Superior; Cnpq; Narodowe Centrum Nauki; Horizon 2020. Our news correspondents obtained a quote from the research from University of Brasilia: “These datasets represent a resource for real-time condition monitoring, enabling the precise detection and diagnosis of system anomalies. This paper aims to enrich the scientific community’s database on structural dynamics and experimental methodologies pertinent to system modelling. Leveraging experimental measurements obtained from mass-reinforced beams, these datasets validate numerical models, refine identification techniques, quantify uncertainties, and continuously foster machine learning algorithms’ evolution to monitor structural integrity. Furthermore, the beam dataset is data-driven and can be used to develop and test innovative structural health monitoring strategies, specifically identifying damages and anomalies within intricate structural frameworks. Supplemental datasets like Mass-position and damage index introduce parametric uncertainty into experimental and damage identification metrics. Thereby offering valuable insights to elevate the efficacy of monitoring and control techniques.”
      原文链接:
    • NETL
    • NSTL
    • Elsevier

    University of Naples Federico Ⅱ Reports Findings in Machine Learning (Evaluation of different machine learning algorithms for predicting the length of stay in the emergency departments: a single-centre study)

    64-64页
    查看更多>>摘要：New research on Machine Learning is the subject of a report. According to news reporting originating in Naples, Italy, by NewsRx journalists, research stated, “Recently, crowding in emergency departments (EDs) has become a recognised critical factor impacting global public healthcare, resulting from both the rising supply/demand mismatch in medical services and the paucity of hospital beds available in inpatients units and EDs. The length of stay in the ED (ED-LOS) has been found to be a significant indicator of ED bottlenecks.” The news reporters obtained a quote from the research from the University of Naples Federico Ⅱ, “The time a patient spends in the ED is quantified by measuring the ED-LOS, which can be influenced by inefficient care processes and results in increased mortality and health expenditure. Therefore, it is critical to understand the major factors influencing the ED-LOS through forecasting tools enabling early improvements. The purpose of this work is to use a limited set of features impacting ED-LOS, both related to patient characteristics and to ED workflow, to predict it. Different factors were chosen (age, gender, triage level, time of admission, arrival mode) and analysed. Then, machine learning (ML) algorithms were employed to foresee ED-LOS. ML procedures were implemented taking into consideration a dataset of patients obtained from the ED database of the ‘San Giovanni di Dio e Ruggi d’Aragona’ University Hospital (Salerno, Italy) from the period 2014-2019. For the years considered, 496,172 admissions were evaluated and 143,641 of them (28.9%) revealed a prolonged ED-LOS. Considering the complete data (48.1% female vs. 51.9% male), 51.7% patients with prolonged ED-LOS were male and 47.3% were female. Regarding the age groups, the patients that were most affected by prolonged ED-LOS were over 64 years. The evaluation metrics of Random Forest algorithm proved to be the best; indeed, it achieved the highest accuracy (74.8%), precision (72.8%), and recall (74.8%) in predicting ED-LOS. Different variables, referring to patients’ personal and clinical attributes and to the ED process, have a direct impact on the value of ED-LOS.”
      原文链接:
    • NETL
    • NSTL
    • Frontiers Media Sa

    Reports Summarize Machine Learning Research from Hassan Ⅱ University (A Holistic Review of Machine Learning Adversarial Attacks in IoT Networks)

    64-64页
    查看更多>>摘要：Investigators discuss new findings in artificial intelligence. According to news reporting originating from Casablanca, Morocco, by NewsRx correspondents, research stated, “With the rapid advancements and notable achievements across various application domains, Machine Learning (ML) has become a vital element within the Internet of Things (IoT) ecosystem.” Our news correspondents obtained a quote from the research from Hassan Ⅱ University: “Among these use cases is IoT security, where numerous systems are deployed to identify or thwart attacks, including intrusion detection systems (IDSs), malware detection systems (MDSs), and device identification systems (DISs). Machine Learning-based (ML-based) IoT security systems can fulfill several security objectives, including detecting attacks, authenticating users before they gain access to the system, and categorizing suspicious activities. Nevertheless, ML faces numerous challenges, such as those resulting from the emergence of adversarial attacks crafted to mislead classifiers. This paper provides a comprehensive review of the body of knowledge about adversarial attacks and defense mechanisms, with a particular focus on three prominent IoT security systems: IDSs, MDSs, and DISs. The paper starts by establishing a taxonomy of adversarial attacks within the context of IoT. Then, various methodologies employed in the generation of adversarial attacks are described and classified within a two-dimensional framework.”
      原文链接:
    • NETL
    • NSTL
    • Mdpi

    Research from University of Sheffield Has Provided New Study Findings on Machine Learning (Material informatics for functional magnetic material discovery)

    65-66页
    查看更多>>摘要：Data detailed on artificial intelligence have been presented. According to news originating from Sheffield, United Kingdom, by NewsRx correspondents, research stated, “Functional magnetic materials are used in a wide range of “green” applications, from wind turbines to magnetic refrigeration.” Funders for this research include Leverhulme Trust; Henry Royce Institute; Engineering And Physical Sciences Research Council. Our news reporters obtained a quote from the research from University of Sheffield: “Often the magnetic materials used contain expensive and/or scarce elements, making them unsuitable for long term solutions. Further, traditional material discovery is a slow and costly process, which can take over 10 years. Material informatics is a growing field, which combines informatics, machine learning (ML) and high-throughput experiments to rapidly discover new materials.”
      原文链接:
    • NETL
    • NSTL

    Researcher from Chongqing University of Posts and Telecommunications Discusses Findings in Machine Learning (Extracting Shrubland in Deserts from Medium-Resolution Remote-Sensing Data at Large Scale)

    66-67页
    查看更多>>摘要：Investigators publish new report on artificial intelligence. According to news originating from Chongqing, People’s Republic of China, by NewsRx editors, the research stated, “Shrubs are important ecological barriers in desert regions and an important component of global carbon estimation.” Financial supporters for this research include National Key Research And Development Program of China; Science And Technology Fundamental Resources Investigation Program. Our news correspondents obtained a quote from the research from Chongqing University of Posts and Telecommunications: “However, the shrubland in deserts has been hardly presented, although many high-quality land cover datasets with a 10 m scale based on remote-sensing data have been publicly released products. Therefore, the underestimation of carbon storage is inevitable with the absence of desert shrublands. The existing land-cover datasets have been analyzed and compared, and it has been found that the reason for missing the shrubland in deserts is mainly indued by the absence of shrubland samples, which are easy to neglect and difficult to retrieve. In this study, we developed a semi-automatic method to extract shrubland samples in deserts as the updated input for the machine-learning method. Firstly, the initial samples of desert shrublands were identified from the very high spatial-resolution (0.3 0.5 m) imagery on GEE, and the maximum NDVI from Sentinel-2 was used for double-checking. Secondly, a feature-based method was used to learn the feature from the initial samples and a similarity-based searching method was employed to automatically expand the samples.”
      原文链接:
    • NETL
    • NSTL
    • Mdpi

    Tongji University Reports Findings in Personalized Medicine (Development and validation of a pathomics model for predicting CXCL8 expression and prognosis with machine learning in head and neck cancer)

    67-68页
    查看更多>>摘要：New research on Drugs and Therapies - Personalized Medicine is the subject of a report. According to news reporting originating in Shanghai, People’s Republic of China, by NewsRx journalists, research stated, “It is imperative to investigate a method to indicate prognosis and to seek new biological markers for personalized medicine for head and neck squamous cell carcinoma (HNSCC) patients. Pathomics based on quantitative medical imaging analysis has emerged lately.” The news reporters obtained a quote from the research from Tongji University, “CXCL8, a crucial inflammatory cytokine, is reflected in overall survival (OS). This study aimed to explore associations of the CXCL8 mRNA expression with pathom ics features and investigate the underlying biology of CXCL8. Clinical information and TPM(transcripts per million) mRNA sequencing data were obtained from the TCGA TCGA- HNSCC data set. The correlation between CXCL8 mRNA expression and patients’ survival rate was identified using the Kaplan Kaplan-Meier survival curve. We retrospectively analyzed 313 samples diagnosed with HNSCC in the TCGA database. Pathomics features were extracted from HE images, then using mRMR_RFE method and screened by the Logistic Regression(LR) algorithm. According to Kaplan Kaplan-Meier curves, the high expression of CXCL8 had a significant association with OS deterioration. The LR pathomics model integrated 16 radiomics features identified by the mRMR_RFE method in the training set and performed well in the testing set. The calibration plots revealed that the high gene expression probability predicted by the pathomics model had a decent agreement with actual observation. And the model has high clinical practicability. The pathomics model reflected by CXCL8 mRNA expression is an effective tool for predicting the prognosis in HNSCC patients and can assist clinical decisiondecisionmaking.”
      原文链接:
    • NETL
    • NSTL