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    New Artificial Intelligence Findings from Thomas Jefferson National Accelerator Facility Described (Charged Track Reconstruction with Artificial Intelligence fo r CLAS12)

    76-76页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New study results on artificial intell igence have been published. According to news reporting out of the Thomas Jeffer son National Accelerator Facility by NewsRx editors, research stated, “In this p aper, we present the results of charged particle track reconstruction in CLAS12 using artificial intelligence.” Our news editors obtained a quote from the research from Thomas Jefferson Nation al Accelerator Facility: “In our approach, we use neural networks working togeth er to identify tracks based on the raw signals in the Drift Chambers. A Convolut ional Auto-Encoder is used to de-noise raw data by removing the hits that do not satisfy the patterns for tracks, and second Multi-Layer Perceptron is used to i dentify tracks from combinations of clusters in the drift chambers. Our method i ncreases the tracking efficiency by 50% for multi-particle final s tates already conducted experiments. The de-noising results indicate that future experiments can run at higher luminosity without degradation of the data qualit y.”
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    Studies Conducted at Jiangnan University on Robotics and Automation Recently Rep orted (Adaptive Lifelong Multi-agent Path Finding With Multiple Priorities)

    77-78页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Research findings on Robotics - Robotics and Auto mation are discussed in a new report. According to news reporting originating in Wuxi, People’s Republic of China, by NewsRx journalists, research stated, “In t his letter, we introduce the Lifelong Evaluation-Based Large Neighborhood Search (LEB-LNS) algorithm designed to address the Lifelong Adaptive Multiple Prioriti es Multi-Agent Path Finding (LAMPMAPF) challenge. This challenge involves agent s that must navigate from one location to another across varying priority levels , constrained by limited calculation time for each interval.” Financial support for this research came from Yangtze River Delta Sci-Tech innov ation Community Joint Research. The news reporters obtained a quote from the research from Jiangnan University, “Initially, a gammabased evaluation function is utilized to determine the signi ficance of different priority levels. Following this, the evaluation led to the development of the Evaluation-Based LNS (EB-LNS) Algorithm, tailored for the Ada ptive Multiple Priorities MAPF (AMP-MAPF) issue. By integrating task assignment, we further extend LEB-LNS algorithm for the LAMP-MAPF problem. The efficacy of LEB-LNS algorithm is verified through simulations conducted on fulfillment and s orting center maps, supplemented by real-world experiments.”
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    Data on Machine Learning Reported by Researchers at Polytechnic University Milan (Machine Learning Techniques for Diagrid Building Design: Architectural-structu ral Correlations With Feature Selection and Data Augmentation)

    78-79页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Current study results on Machine Learn ing have been published. According to news reporting originating from Milan, Ita ly, by NewsRx correspondents, research stated, “Artificial intelligence (AI) and machine learning (ML) techniques are transforming building engineering. This wo rk goes through the critical role of architectural parameters in influencing the structural responses of tall buildings, with a special focus on diagrid structu res.” Our news editors obtained a quote from the research from Polytechnic University Milan, “The main aim of this study is to demonstrate how ML can improve the earl y design phase of diagrid buildings. Using a small, initially collected data set , enhanced through data augmentation, the classification of diagrid buildings in terms of design feasibility is investigated. This study identifies key architec tural and structural parameters, employing various filter and wrapper methods fo r feature selection. The results show that our methods are effective in producin g high -quality synthetic data, maintaining stable learning accuracies, and esta blishing accurate and robust relationships between architectural parameters and structural responses in diagrid buildings.”
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    Findings from Wuhan University of Technology Reveals New Findings on Machine Lea rning (Machine Learning Assisted Prediction of the Phonon Cutoff Frequency of Ab o3 Perovskite Materials)

    79-80页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Current study results on Machine Learn ing have been published. According to news originating from Wuhan, People’s Repu blic of China, by NewsRx correspondents, research stated, “One of the phonon pro perties, the phonon cutoff frequency, pertains to the vibration frequency of the strongest bond in a material, and it has a direct impact on the dielectric brea kdown strength. In this study, the accurate prediction of the phonon cutoff freq uency was achieved using the Light Gradient Boosting Machine (LightGBM) methodol ogy, utilizing only 15 features related to the structural and elemental informat ion of materials.” Financial support for this research came from National Key Research and Devel- o pment Program of China. Our news journalists obtained a quote from the research from the Wuhan Universit y of Technology, “The performance of the LightGBM model yielded R2 of 0.973, RMS E of 2.214, and MAE of 1.289, surpassing other models by a significant margin. F eature analysis revealed a close correlation between the phonon cutoff frequency and the minimum of atomic number among the elements in the composition through SHapley Additive exPlanations (SHAP).”
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    New Findings from Institute of Nanotechnology in the Area of Machine Learning Pu blished (Highly Sensitive and Selective Detection of Dimethyl Methyl Phosphonate with Copolymer-Based QCM Sensors)

    80-80页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Investigators publish new report on artificial in telligence. According to news reporting out of the Institute of Nanotechnology b y NewsRx editors, research stated, “In this work, the volatile organic compounds (VOCs) sensing properties of a quartz crystal microbalance (QCM) transducer coa ted with six different poly(3-methylthiophene) (P3MT) copolymerized with polypyr role (PPy) are investigated.” Our news editors obtained a quote from the research from Institute of Nanotechno logy: “The sensor preparation involves the electrochemical deposition of P3MT, P Py, and P3MT-co-PPy on Au-coated QCM transducers by electrochemical deposition t echniques with a three-electrode cell. The structural properties of the copolyme r films are characterized using scanning electron microscopy, and their oxidatio n/reduction behavior is investigated through cyclic voltammetry. The copolymer-b ased QCM sensors exhibit high sensitivity and selectivity to dimethyl methyl pho sphonate and benzonitrile, even at low concentrations ( <1 p pm) at room temperature. Langmuir, Freundlich, Temkin, and Dubinin-Radushkevich adsorption isotherms are studied to understand the VOCs sensing mechanism machin e learning classification algorithms including quadratic discriminant (QD), neur al nets, K-nearest neighbors, linear discriminant, and support vector machines a re applied to classify the sensor responses for the 12 different analytes. With the help of machine learning algorithms, tested analytes are successfully classi fied into their groups. The highest accuracy of 97.34% is achieved using the QD method.”
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    University Hospital Southampton NHS Foundation Trust Reports Findings in Uretero scopy (A machine learning approach using stone volume to predict stone-free stat us at ureteroscopy)

    81-81页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Surgical Procedures - Ureteroscopy is the subject of a report. According to news originating from Sout hampton, United Kingdom, by NewsRx correspondents, research stated, “To develop a predictive model incorporating stone volume along with other clinical and radi ological factors to predict stone-free (SF) status at ureteroscopy (URS). Retros pective analysis of patients undergoing URS for kidney stone disease at our inst itution from 2012 to 2021.” Our news journalists obtained a quote from the research from University Hospital Southampton NHS Foundation Trust, “SF status was defined as stone fragments <2 mm at the end of the procedure confirmed endoscopically and no evidence of st one fragments > 2 mm at XR KUB or US KUB at 3 months fol low up. We specifically included all non-SF patients to optimise our algorithm f or identifying instances with residual stone burden. SF patients were also rando mly sampled over the same time period to ensure a more balanced dataset for ML p rediction. Stone volumes were measured using preprocedural CT and combined with 19 other clinical and radiological factors. A bagged trees machine learning mode l with cross-validation was used for this analysis. 330 patients were included ( SF: n = 276, not SF: n = 54, mean age 59.5 ± 16.1 years). A fivefold cross valid ated RUSboosted trees model has an accuracy of 74.5% and AUC of 0. 82. The model sensitivity and specificity were 75% and 72.2% respectively. Variable importance analysis identified total stone volume (17.7% of total importance), operation time (14.3%), age (12.9% ) and stone composition (10.9%) as important factors in predicting non-SF patients. Single and cumulative stone size which are commonly used in cur rent practice to guide management, only represented 9.4% and 4.7% of total importance, respectively. Machine learning can be used to predict patie nts that will be SF at the time of URS. Total stone volume appears to be more im portant than stone size in predicting SF status.”
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    University Teaching Hospital Researcher Reports on Findings in Machine Learning (Predictive Machine Learning Model For Mechanical Dilatation in Transvenous Lead Extraction Procedures)

    82-82页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Research findings on artificial intell igence are discussed in a new report. According to news reporting originating fr om the University Teaching Hospital by NewsRx correspondents, research stated, “ Transvenous lead extraction (TLE) remains a procedure that requires a high level of expertise, with a doubled risk of death and clinical failure when performed in low-volume centers compared to highvolume ones. The aim of this study was to create a machine learning (ML)-based risk stratification system for predicting the need for mechanical dilatation in patients undergoing TLE due to infection.” The news reporters obtained a quote from the research from University Teaching H ospital: “We designed a ML-based risk stratification system trained with data fr om our registry to predict the need for mechanical dilatation in patients underg oing TLE for infection. An extensive evaluation of 5 different ML models (k-near est neighbors, support vector machine, decision tree, and decision tree ensemble s, such as random forest and gradient boosting machine) was conducted to identif y a classifier with the highest potential to correctly predict previously unseen patients. Data to train the model was extracted from our 25-year registry of pa tients undergoing TLE (June 1998 - March 2023), for a total of 491 patients (77. 8% male; age 69.7 ± 12.8 years) and 938 leads (ICD 21.2% ; pacing 78.8%; indwelling time 61 ± 60 months) removed with succes s in 100% of cases. Each patient was represented by a set of 21 at tributes (14 clinical, 7 device-related). Manual traction (MT) was used in 27.5% of cases, and mechanical dilatation (MD) was employed in the remaining 72.5% of cases. 5-fold nested cross validation was used to estimate performances: in t urn, 393 patients were used for training and model selection, and 98 patients we re used for independent testing. According to the evaluation, Gradient Boosting Machine performed best, achieving test accuracy of 89% (+/- 2% std. dev.), test sensitivity of 95% (+/- 3% std. dev .), test specificity of 73% (+/- 8% std. dev.), test AUROC of 92% (+/- 1% std. dev.). A further interpre tability analysis on the best performing decision tree was conducted, showing re markable adherence between the internal decisions taken by the model to make pre dictions and the current clinical practice for TLE.”
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    Researchers from Hebei University of Technology Report Details of New Studies an d Findings in the Area of Robotics (Biofusion Design and Parameter Optimization for a Novel Passive Assisted Knee Exoskeleton Robot Based On Eight-bar Mechanism )

    83-83页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on Ro botics. According to news reporting originating in Tianjin, People’s Republic of China, by NewsRx journalists, research stated, “In an effort to alleviate the i ssue of knee joint fatigue and injury during lower limb ambulation, a novel pass ive assisted exoskeleton robot with human-machine interaction is investigated to assist the movement of the human knee joint. The design of the exoskeleton conf iguration takes into consideration the physiological structure and gait function of the knee joint, ensuring that it satisfies the requirements for motion, forc e, and gait function of the knee joint.” Funders for this research include National Natural Science Foundation of China ( NSFC), Natural Science Foundation of Hebei Province, Major Scientific and Techno logical Achievements Transformation Project in Hebei Province, Central Governmen t guides basic research projects of Local Science and Technology Development Fun ds.
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    New Machine Learning Data Have Been Reported by Researchers at Macalester Colleg e (Machine Learning Approaches Delimit Cryptic Taxa In a Previously Intractable Species Complex)

    84-85页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Machine Learning is th e subject of a report. According to news reporting from St. Paul, Minnesota, by NewsRx journalists, research stated, “Cryptic species are not diagnosable via mo rphological criteria, but can be detected through analysis of DNA sequences. A n umber of methods have been developed for identifying species based on genetic da ta; however, these methods are prone to over-splitting taxa with extreme populat ion structure, such as dispersal-limited organisms.” Financial supporters for this research include Macalester College, Arnold and Ma bel Beckman Foundation, National Science Foundation (NSF).
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    Chinese Academy of Agricultural Sciences Reports Findings in Machine Learning (E xploring salt tolerance mechanisms using machine learning for transcriptomic ins ights: case study in Spartina alterniflora)

    85-86页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Machine Learning is th e subject of a report. According to news reporting originating from Beijing, Peo ple’s Republic of China, by NewsRx correspondents, research stated, “Salt stress poses a significant threat to global cereal crop production, emphasizing the ne ed for a comprehensive understanding of salt tolerance mechanisms. Accurate func tional annotations of differentially expressed genes are crucial for gaining ins ights into the salt tolerance mechanism.” Our news editors obtained a quote from the research from the Chinese Academy of Agricultural Sciences, “The challenge of predicting gene functions in under-stud ied species, especially when excluding infrequent GO terms, persists. Therefore, we proposed the use of NetGO 3.0, a machine learning-based annotation method th at does not rely on homology information between species, to predict the functio ns of differentially expressed genes under salt stress. , a halophyte with salt glands, exhibits remarkable salt tolerance, making it an excellent candidate for in-depth transcriptomic analysis. However, current research on the transcriptom e under salt stress is limited. In this study we used as an example to investiga te its transcriptional responses to various salt concentrations, with a focus on understanding its salt tolerance mechanisms. Transcriptomic analysis revealed s ubstantial changes impacting key pathways, such as gene transcription, ion trans port, and ROS metabolism. Notably, we identified a member of the gene family in , showing convergent selection with the rice ortholog. Additionally, our genome- wide analyses explored alternative splicing responses to salt stress, providing insights into the parallel functions of alternative splicing and transcriptional regulation in enhancing salt tolerance in. Surprisingly, there was minimal over lap between differentially expressed and differentially spliced genes following salt exposure.”
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