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    Report Summarizes Robotics Study Findings from Jimei University (Path Planning f or Mobile Robots Using Transfer Reinforcement Learning)

    68-69页
    查看更多>>摘要：Current study results on robotics have been published. According to news reporting from Fujian, People's Republic of C hina, by NewsRx journalists, research stated, "The path planning of mobile robot s helps robots to perceive environment using the information obtained from senso rs and plan a route to reach the target." Funders for this research include Young And Middle-aged Teachers in Fujian Provi nce; Department of Education of Fujian Province; National Natural Science Founda tion Cultivation Program of Jimei University. The news journalists obtained a quote from the research from Jimei University: " With the increasing difficulty of task, the environment the mobile robots face b ecomes more and more complex. Traditional path planning methods can no longer me et the requirements of mobile robot navigation in complex environment. Deep rein forcement learning (DRL) is introduced into robot navigation However, it may be time-consuming to train DRL model when the environment is very complex and the e xisting environment may differ from the unknown environment. In order to handle the robot navigation in heterogeneous environment, this paper utilizes deep tran sfer reinforcement learning (DTRL) for mobile robot path planning. Compared with DRL, DTRL does not require the distribution of the existing environment is the same as that of the unknown environment. Additionally, DTRL can transfer the kno wledge of existing model to new scenario to reduce the training time."
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    Rochester Institute of Technology Researcher Updates Current Study Findings on M achine Learning (Regulating Modality Utilization within Multimodal Fusion Networ ks)

    69-70页
    查看更多>>摘要：A new study on artificial intelligence is now available. According to news originating from Rochester, New York, by Ne wsRx correspondents, research stated, "Multimodal fusion networks play a pivotal role in leveraging diverse sources of information for enhanced machine learning applications in aerial imagery." Financial supporters for this research include National Geospatial-intelligence Agency; Air Force Office of Scientific Research; National Science Foundation. Our news reporters obtained a quote from the research from Rochester Institute o f Technology: "However, current approaches often suffer from a bias towards cert ain modalities, diminishing the potential benefits of multimodal data. This pape r addresses this issue by proposing a novel modality utilizationbased training method for multimodal fusion networks. The method aims to guide the network's ut ilization on its input modalities, ensuring a balanced integration of complement ary information streams, effectively mitigating the overutilization of dominant modalities. The method is validated on multimodal aerial imagery classification and image segmentation tasks, effectively maintaining modality utilization withi n ±10% of the user-defined target utilization and demonstrating th e versatility and efficacy of the proposed method across various applications. F urthermore, the study explores the robustness of the fusion networks against noi se in input modalities, a crucial aspect in real-world scenarios. The method sho wcases better noise robustness by maintaining performance amidst environmental c hanges affecting different aerial imagery sensing modalities."
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    New Study Findings from University of Sevilla Illuminate Research in Machine Lea rning (Optimized Machine Learning Classifiers for Symptom-Based Disease Screenin g)

    70-71页
    查看更多>>摘要：New research on artificial intelligenc e is the subject of a new report. According to news reporting from Seville, Spai n, by NewsRx journalists, research stated, "This work presents a disease detecti on classifier based on symptoms encoded by their severity." Funders for this research include Ministerio De Ciencia, Innovacion Y Universida des (Spanish Government): Spanish Aei (Agencia Estatal De Investigacion) Project Adicvideo. Our news journalists obtained a quote from the research from University of Sevil la: "This model is presented as part of the solution to the saturation of the he althcare system, aiding in the initial screening stage. An open-source dataset i s used, which undergoes pre-processing and serves as the data source to train an d test various machine learning models, including SVM, RFs, KNN, and ANNs. A thr ee-phase optimization process is developed to obtain the best classifier: first, the dataset is pre-processed; secondly, a grid search is performed with several hyperparameter variations to each classifier; and, finally, the best models obt ained are subjected to additional filtering processes. The best-results model, s elected based on the performance and the execution time, is a KNN with 2 neighbo rs, which achieves an accuracy and F1 score of over 98%."
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    Cyprus Institute of Neurology and Genetics Reports Findings in Personalized Medi cine (Combining clinical and molecular data for personalized treatment in acute myeloid leukemia: A machine learning approach)

    71-72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 ou t of Nicosia, Cyprus, by NewsRx editors, research stated, "The standard of care in Acute Myeloid Leukemia patients has remained essentially unchanged for nearly 40 years. Due to the complicated mutational patterns within and between individ ual patients and a lack of targeted agents for most mutational events, implement ing individualized treatment for AML has proven difficult." Our news journalists obtained a quote from the research from the Cyprus Institut e of Neurology and Genetics, "We reanalysed the BeatAML dataset employing Machin e Learning algorithms. The BeatAML project entails patients extensively characte rized at the molecular and clinical levels and linked to drug sensitivity output s. Our approach capitalizes on the molecular and clinical data provided by the B eatAML dataset to predict the ex vivo drug sensitivity for the 122 drugs evaluat ed by the project. We utilized ElasticNet, which produces fully interpretable mo dels, in combination with a two-step training protocol that allowed us to narrow down computations. We automated the genes' filtering step by employing two metr ics, and we evaluated all possible data combinations to identify the best traini ng configuration settings per drug. We report a Pearson correlation across all d rugs of 0.36 when clinical and RNA sequencing data were combined, with the best-performing models reaching a Pearson correlation of 0.67. When we trained using the datasets in isolation, we noted that RNA Sequencing data (Pearson: 0.36) att ained three times the predictive power of whole exome sequencing data (Pearson: 0.11), with clinical data falling somewhere in between (Pearson 0.26). Lastly, w e present a paradigm of clinical significance. We used our models' prediction as a drug sensitivity score to rank an individual's expected response to treatment . We identified 78 patients out of 89 (88 %) that the proposed drug was more potent than the administered one based on their ex vivo drug sensitivi ty data."
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    Studies from Queen Mary University of London Further Understanding of Machine Le arning (Synergizing Machine Learning & Symbolic Methods: a Survey On Hybrid Approaches To Natural Language Processing)

    72-73页
    查看更多>>摘要：Investigators discuss new findings in Machine Learning. According to news reporting out of London, United Kingdom, by NewsRx editors, research stated, "The advancement of machine learning and symbol ic approaches have underscored their strengths and weaknesses in Natural Languag e Processing (NLP). While machine learning approaches are powerful in identifyin g patterns in data, they often fall short in learning commonsense and the factua l knowledge required for the NLP tasks." Financial supporters for this research include UK Research and Innovation as par t of Marie Sklodowska-Curie Actions (MSCA Hybrid Intelligence to monitor, promot e, and analyze transformations in good democracy practices), European Union (EU).
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    New Findings from Nanyang Technological University Describe Advances in Artifici al Intelligence (Managing a Patient With Uveitis In the Era of Artificial Intell igence: Current Approaches, Emerging Trends, and Future Perspectives)

    73-74页
    查看更多>>摘要：Fresh data on Artificial Intelligence are presented in a new report. According to news reporting originating from Sing apore, Singapore, by NewsRx correspondents, research stated, "The integration of artificial intelligence (AI) with healthcare has opened new avenues for diagnos ing, treating, and managing medical conditions with remarkable precision. Uveiti s, a diverse group of rare eye conditions characterized by inflammation of the u veal tract, exemplifies the complexities in ophthalmology due to its varied caus es, clinical presentations, and responses to treatments." Financial support for this research came from National Medical Research Council, Singapore. Our news editors obtained a quote from the research from Nanyang Technological U niversity, "Uveitis, if not managed promptly and effectively, can lead to signif icant visual impairment. However, its management requires specialized knowledge, which is often lacking, particularly in regions with limited access to health s ervices. AI's capabilities in pattern recognition, data analysis, and predictive modelling offer significant potential to revolutionize uveitis management. AI c an classify disease etiologies, analyze multimodal imaging data, predict outcome s, and identify new therapeutic targets. However, transforming these AI models i nto clinical applications and meeting patient expectations in-volves overcoming challenges like acquiring extensive, annotated datasets, ensuring algorithmic t ransparency, and validating these models in real-world settings. This review del ves into the complexities of uveitis and the current AI landscape, discussing th e development, opportunities, and challenges of AI from theoretical models to be dside application. It also examines the epidemiology of uveitis, the global shor tage of uveitis specialists, and the disease's socioeconomic impacts, underlinin g the critical need for AI-driven approaches."
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    University of Florence Reports Findings in Artificial Intelligence (Transitionin g the production of lipidic mesophase-based delivery systems from lab-scale to r obust industrial manufacturing following a risk-based quality by design approach ...)

    74-75页
    查看更多>>摘要：New research on Artificial Intelligenc e is the subject of a report. According to news reporting originating in Florenc e, Italy, by NewsRx journalists, research stated, "Lipidic mesophase drug carrie rs have demonstrated the capacity to host and effectively deliver a wide range o f active pharmaceutical ingredients, yet they have not been as extensively comme rcialized as other lipid-based products, such as liposomal delivery systems. Ind eed, scientists are primarily focused on investigating the physics of these syst ems, especially in biological environments." The news reporters obtained a quote from the research from the University of Flo rence, "Meanwhile, the production methods remain less advanced, and researchers are still uncertain about how the manufacturing process might affect the quality of formulations. Bringing these products to the market will require an industri al translation process. In this scenario, we have developed a robust strategy to produce lipidic mesophase-based drug delivery systems using a dual-syringe setu p. We identified four critical process parameters in the newly developed method (dual-syringe method), in comparison to eight in the standard production method (gold standard), and we defined their optimal limits following a Quality by Desi gn approach."
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    Findings on Machine Learning Discussed by Investigators at Chinese Academy of Sc iences (Machine Learning Enhanced Rigiflex Pillarmembrane Triboelectric Nanogen erator for Universal Stereoscopic Recognition)

    75-76页
    查看更多>>摘要：Researchers detail new data in Machine Learning. According to news reporting out of Beijing, People's Republic of Chin a, by NewsRx editors, research stated, "The advent of the artificial intelligenc e (AI) and Internet of Things (IoTs) era has spurred a surge in the analysis of voluminous data gathered from myriad distributed sensors. This endeavor is prima rily aimed at executing sophisticated recognition functions, which frequently de mand excessive energy consumption." Financial supporters for this research include National Key Research and Develop ment (R & D) Program from Ministry of Science and Technology, Nati onal Natural Science Foundation of China (NSFC), Beijing Municipal Science & Technology Commission, Fundamental Research Funds for the Central Universities, Chinese Academy of Sciences.
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    New Robotics Study Findings Recently Were Reported by Researchers at Huazhong Un iversity of Science and Technology (Quantification of Uncertainty In Robot Pose Errors and Calibration of Reliable Compensation Values)

    76-77页
    查看更多>>摘要：Researchers detail new data in Robotic s. According to news reporting out of Wuhan, People's Republic of China, by News Rx editors, research stated, "Due to their inherent characteristics, robots inev itably suffer from pose errors, and accurate prediction is the key to error comp ensation, which facilitates the application of robots in high -precision scenari os. Existing studies almost follow the points -view, and the compensation effect depends entirely on the accuracy of the point prediction, which leads to overco nfident prediction results." Funders for this research include Foundation of China, National Key Research & Development Program of China, Basic Science Center of China.
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    Study Data from Wuhan University Update Understanding of Robotics (Trajectory Tr acking Control of a Wall-building Robot In an Uncertain Viscoelastic Environment )

    77-78页
    查看更多>>摘要：New research on Robotics is the subjec t of a report. According to news reporting originating from Wuhan, People's Repu blic of China, by NewsRx correspondents, research stated, "During the bricklayin g process, the wall-building robot is not only affected by viscoelastic contact force, but also affected by dynamic modeling errors and external disturbances, w hich leads to low trajectory tracking accuracy and poor control performance, res ulting in a low bricklaying accuracy. To solve the problem, an adaptive nonsingu lar fast terminal sliding mode robust trajectory tracking control strategy based on recursive fuzzy wavelet neural network (RFWNN) is proposed." Funders for this research include Natural Science Foundation Innova-tion Group Project in Hubei Province, National Natural Science Foundation of China (NSFC), High -Performance Computing Center of Wuhan University of Science and Technology .
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