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    Researchers from University Sains Malaysia Provide Details of New Studies and Fi ndings in the Area of Machine Translation (Postediting challenges in Chinese-to -English neural machine translation of movie subtitles)

    55-56页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Fresh data on machine translation are presented in a new report. According to news reporting out of Penang, Malaysia, by NewsRx editors, research stated, “Subtitle translation has been a longstandin g factor hindering the overseas development of Chinese movies. The potential of using Neural Machine Translation (NMT) as an innovative solution has yet to be s tudied.” The news reporters obtained a quote from the research from University Sains Mala ysia: “This case study aims to integrate Google Neural Machine Translation (GNMT ) into the Chinese-into-English subtitle translation of Chinese movies. The rese arch identifies errors in GNMT-generated subtitles per Pedersen’s FAR model and develops post-editing (PE) recommendations to address these errors. Firstly, the Chinese subtitles, human-translated subtitles, and GNMT-generated subtitles of a Chinese movie were collected. Then, the FAR model-based error analysis was con ducted to explore the errors’ features. Lastly, PE recommendations were proposed accordingly to modify these errors. Approximately a quarter of all subtitles co ntain errors, with functional equivalence errors the most prevalent (about half) , followed by acceptability errors (about a third) and readability errors (14% ). Regarding the severity of errors, standard errors rank first (42% ), followed by serious errors (30%) and minor errors (28% ).”
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    Researchers from Hebei University of Technology Report Findings in Robotics (A T rajectory Planning Method for Robotic Arms Based On Improved Dynamic Motion Prim itives)

    56-57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 originating from Tianjin, People’s Republic of China, by NewsRx correspondents, research stated, “Purpose- Traditional robot arm traject ory planning methods have problems such as insufficient generalization performan ce and low adaptability. This paper aims to propose a method to plan the robot a rm’s trajectory using the trajectory learning and generalization characteristics of dynamic motion primitives (DMPs).Design/methodology/approachThis study align s multiple demonstration motion primitives using dynamic time warping; use the G aussian mixture model and Gaussian mixture regression methods to obtain the idea l primitive trajectory actions.”
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    Department of Urology Reports Findings in Kidney Transplants (Robotic-assisted n ative pyeloureterostomy with indocyanine green, after kidney transplantation)

    57-58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 Transplant Medicine - Kidney Transplants is the subject of a report. According to news reporting out o f Meaux, France, by NewsRx editors, research stated, “Postoperative ureteral str ictures and vesicoureteral reflux after ureteroneocystostomy for kidney transpla nt can be managed by endoscopic procedures like balloon dilation and endoscopic injections. Complicated/recurrent cases, however, are usually managed by reconst ructive surgery.” Our news journalists obtained a quote from the research from the Department of U rology, “We hereby highlight our technique of robotic-assisted native pyelourete rostomy with indocyanine green (ICG). A 57- year-old woman, diagnosed with grade 4 vesicoureteral reflux on her transplanted kidney, was considered a candidate f or ureteral reimplantation. After an endoscopic part, where the ICG is inserted into the renal pelvis, we proceed with the robotic native pyeloureterostomy. The renal pelvis of the transplanted kidney was identified with the help of the ICG in firefly mode. After the dissection of the graft pelvis, we performed a tensi on-free pyeloureterostomy using the native ureter. The postoperative course was uneventful and the patient was discharged on the third postoperative day. Roboti c-assisted pyelo-ureterostomy appears as a safe and efficient technique for mana gement of complicated urological complications postrenal transplantation using t he native ureter. Intrapelvic ICG injection, not possible with open surgery, hel ps identifying the grafted pelvis thus reducing operative time and avoiding unne cessary dissection of the vascular hilum of the graft.”
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    Reports Outline Robotics Study Findings from University of Colorado Boulder (Fem tosecond Laser Fabricated Nitinol Living Hinges for Millimeter-sized Robots)

    58-58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 Boulder, Colorado, by NewsRx journalists, research stated, “Nitinol is a smart material that can be used as a n actuator, a sensor, or a structural element, and has the potential to signific antly enhance the capabilities of microrobots. Femtosecond laser technology can be used to process nitinol while avoiding heat-affected zones (HAZ), thus retain ing superelastic properties.” Financial support for this research came from Paul M. Rady Mechanical Engineerin g Department.
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    Fort Valley State University Researchers Advance Knowledge in Artificial Intelli gence (From Plants to Pixels: The Role of Artificial Intelligence in Identifying Sericea Lespedeza in Field-Based Studies)

    59-59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 artificial in telligence have been published. According to news reporting from Fort Valley, Ge orgia, by NewsRx journalists, research stated, “The increasing use of convolutio nal neural networks (CNNs) has brought about a significant transformation in num erous fields, such as image categorization and identification. In the developmen t of a CNN model to classify images of sericea lespedeza [SL; * * Lespedeza cuneata* * (Dum-Cours) G.” Financial supporters for this research include Usda-national Institute of Food A nd Agriculture.
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    Research Conducted at Chinese Academy of Sciences Has Updated Our Knowledge abou t Machine Learning (Research On Hot Deformation Behavior of Cu-ti Alloy Based On Machine Learning Algorithms and Microalloying)

    60-60页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Fresh data on Machine Learning are presented in a new report. According to news reporting out of Shenyang, People’s Republic of C hina, by NewsRx editors, research stated, “High strength and high elasticity cop per alloys have good application prospects due to their excellent mechanical pro perties. The deformation and heat treatment in its preparation process are impor tant steps to ensure the service performance.” Financial supporters for this research include National Natural Science Foundati on of China (NSFC), Science and Tech- nology Research Projects of Sichuan Provin ce.
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    Beijing Institute of Technology Researchers Provide Details of New Studies and F indings in the Area of Machine Learning (DSOMF: A Dynamic Environment Simultaneo us Localization and Mapping Technique Based on Machine Learning)

    61-61页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – New study results on artificial intelligence have been published. According to news reporting from Beijing, People’s Republic of China, by NewsRx journalists, research stated, “To address the challenges of red uced localization accuracy and incomplete map construction demonstrated using cl assical semantic simultaneous localization and mapping (SLAM) algorithms in dyna mic environments, this study introduces a dynamic scene SLAM technique that buil ds upon direct sparse odometry (DSO) and incorporates instance segmentation and video completion algorithms.” Funders for this research include National Natural Science Foundation of China.
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    Reports from Carnegie Mellon University Add New Data to Findings in Robotics (Gr aph-propagation-based Kinematic Algorithm for Inpipe Truss Structure Robots)

    61-62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 Robotics. Acc ording to news originating from Pitts, Georgia,by NewsRx correspondents, resear ch stated, “Robots designed for in-pipe navigation, inspection, and repair requi re flexibility for intricate pipeline traversal and the strength to carry payloa ds. However, conventional wheeled in-pipe robots face challenges in simultaneous ly achieving both substantial flexibility and payload-carrying capacity.” Financial support for this research came from Advanced Research Projects Agency - Energy. Our news journalists obtained a quote from the research from Carnegie Mellon Uni versity, “A superior approach involves utilizing truss robots with redundant joi nts and linkages for pipe shape adaptation and actuation force distribution, pro viding significant advantages for complex pipeline navigation and heavy payload delivery. However, the kinematics of truss robots is computationally expensive f or conventional Jacobian-based algorithms due to their complicated structural co nstraints. To address this limitation, we propose a novel algorithm for efficien t truss-robot-kinematics computation using Graph Propagation (GP) method. Our me thod computes both forward kinematics and Jacobian in a propagative manner. It a lso guarantees geometric constraints with the Sigmoid function as the boundary. In simulation experiments, our algorithm accelerates pipe shape adaptation compu tation by 5.2 similar to 16.4 times compared to finite difference methods. The p ractical feasibility of our method is assessed through physical in-pipe crawling experiments using a truss robot prototype. Additionally, the prototype’s abilit y to carry heavy payloads is demonstrated through payload-carrying experiments, which results in 2 similar to 4 times heavier payload capacity compared to two-w heeled robot approaches. We also showcase the versatility of proposed method in addressing manipulation tasks, indicating its generalizability across diverse ap plications.”
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    University of Chinese Academy of Sciences Reports Findings in Machine Learning ( Identifying the risk factors of ICU-acquired fungal infections: clinical evidenc e from using machine learning)

    62-63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 in Shenzhen, Peop le’s Republic of China, by NewsRx journalists, research stated, “Fungal infectio ns are associated with high morbidity and mortality in the intensive care unit ( ICU), but their diagnosis is difficult. In this study, machine learning was appl ied to design and define the predictive model of ICU-acquired fungi (ICU-AF) in the early stage of fungal infections using Random Forest.” The news reporters obtained a quote from the research from the University of Chi nese Academy of Sciences, “This study aimed to provide evidence for the early wa rning and management of fungal infections. We analyzed the data of patients with culture-positive fungi during their admission to seven ICUs of the First Affili ated Hospital of Chongqing Medical University from January 1, 2015, to December 31, 2019. Patients whose first culture was positive for fungi longer than 48 h a fter ICU admission were included in the ICU-AF cohort. A predictive model of ICU -AF was obtained using the Least Absolute Shrinkage and Selection Operator and m achine learning, and the relationship between the features within the model and the disease severity and mortality of patients was analyzed. Finally, the relati onships between the ICU-AF model, antifungal therapy and empirical antifungal th erapy were analyzed. A total of 1,434 cases were included finally. We used lasso dimensionality reduction for all features and selected six features with import ance 0.05 in the optimal model, namely, times of arterial catheter, enteral nutr ition, corticosteroids, broadspectrum antibiotics, urinary catheter, and invasiv e mechanical ventilation. The area under the curve of the model for predicting I CU-AF was 0.981 in the test set, with a sensitivity of 0.960 and specificity of 0.990. The times of arterial catheter ( = 0.011, OR = 1.057, 95% C I = 1.053-1.104) and invasive mechanical ventilation ( = 0.007, OR = 1.056, 95% CI = 1.015-1.098) were independent risk factors for antifungal therapy in ICU-AF . The times of arterial catheter ( = 0.004, OR = 1.098, 95%CI = 0.8 55- 0.970) were an independent risk factor for empirical antifungal therapy. The most important risk factors for ICU-AF are the six time-related features of clin ical parameters (arterial catheter, enteral nutrition, corticosteroids, broadspe ctrum antibiotics, urinary catheter, and invasive mechanical ventilation), which provide early warning for the occurrence of fungal infection.”
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    Data on Artificial Intelligence Reported by Researchers at Department of Neurosu rgery (Can Publicly Available Artificial Intelligence Successfully Identify Curr ent Procedural Terminology Codes for Common Procedures In Neurosurgery?)

    64-64页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Researchers detail new data in Artific ial Intelligence. According to news originating from Nutley, New Jersey, by News Rx correspondents, research stated, “- Coding for neurosurgical procedures is a complex process that is dynamically changing year to year, through the annual in troduction and removal of codes and modifiers. The authors hoped to elucidate if publicly available artificial intelligence (AI) could offer solutions for neuro surgeons with regard to coding.” Our news journalists obtained a quote from the research from the Department of N eurosurgery, “Multiple publicly available AI platforms were asked to provide Cur rent Procedural Terminology (CPT) codes and Revenue Value Units (RVU) values for common neurosurgical procedures of the brain and spine with a given indication for the procedure. The responses of platforms were recorded and compared to the currently valid CPT codes used for the procedure and the amount of RVUs that wou ld be gained. Six platforms and Google were asked for the appropriate CPT codes for 10 endovascular, spinal, and cranial procedures each. The highest performing platforms were as follows: Perplexity.AI identified 70% of endova scular, BingAI identified 55% of spinal, and ChatGPT 4.0 with Bing identified 75% of cranial CPT codes. With regard to RVUs, the top performer gained 78% of endovascular, 42% of spinal , and 70% of cranial possible RVUs. With regard to accuracy, AI pl atforms on average outperformed Google (45% vs. 25%, P [ 0.04236). The ability of publicly available AIs to succes sfully code for neurosurgical procedures holds great promise in the future. Futu re development of AI should focus on improving accuracy with regard to CPT codes and providing supporting documentation for its decisions.”
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