期刊,Robotics & Machine Learning Daily News 2024年卷Mar.6期_国家学术搜索

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Studies from Mayo Clinic Further Understanding of Robotics (Robotic-assisted Int ravesical Mesh Excision Following Retropubic Midurethral Sling)

1-1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 from Rochester, Minnesota, by Ne wsRx correspondents, research stated, "Introduction and hypothesisIntravesical m esh is an uncommon complication following synthetic midurethral sling placement. Management options have included endoscopic techniques such as laser ablation o r surgical excision." Our news editors obtained a quote from the research from Mayo Clinic, "We presen t our technique for robotic-assisted excision of intravesical mesh following a r etropubic midurethral sling.MethodsThe patient is a 66-year-old woman with a rem ote history of laser ablation of intraurethral mesh after midurethral sling, and persistent symptomatic intravesical mesh with associated stone at the bladder n eck and right bladder wall. Robotic excision of the intravesical mesh and stone was performed by entering the space of Retzius, carrying the dissection along th e right arm of the retropubic sling, performing two cystotomies to free and remo ve the mesh, and finally closing the cystotomies in two layers.ResultsThe patien t was discharged on postoperative day 1. A cystogram prior to catheter removal s howed no extravasation and a competent bladder neck. She reported no new stress incontinence and had improvement in overactive bladder symptoms.ConclusionsRobot ic excision of intravesical mesh after synthetic midurethral sling was safely pe rformed in this patient who had multiple areas of intravesical mesh."

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Data on Machine Learning Reported by Researchers at Florida Institute of Technol ogy (A Machine Learning Approach To Predict Austenite Finish Temperature In Quat ernary Nitihfpd Smas)

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查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-A new study on Machine Learning is now available. According to news reporting from Melbourne, Florida, by NewsRx journ alists, research stated, "Machine learning (ML) has emerged as a promising tool for the design of multicomponent alloys due to their vast design spaces. Quatern ary NiTiHfPd shape memory alloys (SMAs) possess unique potential to be employed in high-temperature actuation as well as damping systems." The news correspondents obtained a quote from the research from the Florida Inst itute of Technology, "This study presents a machine learning approach using the currently available limited data regime to accelerate research on NiTiHfPd SMAs. To this end, a database of transformation temperatures of NiTiHfPd SMAs was com piled and expanded through compositional and post-processing features of the all oys." According to the news reporters, the research concluded: "Various ML algorithms were utilized to predict the austenite finish temperature of NiTiHfPd SMAs and t hen validated through experiments." This research has been peer-reviewed.

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Researchers from Beijing Jiaotong University Report Recent Findings in Mathemati cs (The Investigation of Data Voting Algorithm for Train Air-braking System Base d On Multi-classification Svm and Anfis)

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查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Investigators discuss new findings in Mathematics . According to news originating from Beijing, People's Republic of China, by New sRx correspondents, research stated, "The pressure data of the train air braking system is of great significance to accurately evaluate its operation state. In order to overcome the influence of sensor fault on the pressure data of train ai r braking system, it is necessary to design a set of sensor fault-tolerant votin g mechanism to ensure that in the case of a pressure sensor fault, the system ca n accurately identify and locate the position of the faulty sensor, and estimate the fault data according to other normal data." Financial support for this research came from National Key R&D Prog ram of China.

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Rajshahi University of Engineering & Technology Reports Findings in Stroke (A stroke prediction framework using explainable ensemble learning)

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查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Cerebrovascular Diseas es and Conditions - Stroke is the subject of a report. According to news reporti ng from Rajshahi, Bangladesh, by NewsRx journalists, research stated, "The death of brain cells occurs when blood flow to a particular area of the brain is abru ptly cut off, resulting in a stroke. Early recognition of stroke symptoms is ess ential to prevent strokes and promote a healthy lifestyle." The news correspondents obtained a quote from the research from the Rajshahi Uni versity of Engineering & Technology, "FAST tests (looking for abno rmalities in the face, arms, and speech) have limitations in reliability and acc uracy for diagnosing strokes. This research employs machine learning (ML) techni ques to develop and assess multiple ML models to establish a robust stroke risk prediction framework. This research uses a stacking-based ensemble method to sel ect the best three machine learning (ML) models and combine their collective int elligence. An empirical evaluation of a publicly available stroke prediction dat aset demonstrates the superior performance of the proposed stacking-based ensemb le model, with only one misclassification. The experimental results reveal that the proposed stacking model surpasses other state-of-the-art research, achieving accuracy, precision, F1-score of 99.99%, recall of 100% , receiver operating characteristics (ROC), Mathews correlation coefficient (MCC ), and Kappa scores 1.0. Furthermore, Shapley's Additive Explanations (SHAP) are employed to analyze the predictions of the black-box machine learning (ML) mode ls. The findings highlight that age, BMI, and glucose level are the most signifi cant risk factors for stroke prediction."

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Huazhong University of Science and Technology Reports Findings in Machine Learni ng (Predicting preterm birth using auto-ML frameworks: a large observational stu dy using electronic inpatient discharge data)

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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 out of Hubei, People's Republ ic of China, by NewsRx editors, research stated, "To develop and compare differe nt AutoML frameworks and machine learning models to predict premature birth. The study used a large electronic medical record database to include 715,962 partic ipants who had the principal diagnosis code of childbirth." Our news journalists obtained a quote from the research from the Huazhong Univer sity of Science and Technology, "Three Automatic Machine Learning (AutoML) were used to construct machine learning models including tree-based models, ensembled models, and deep neural networks on the training sample ( = 536,971). The area under the curve (AUC) and training times were used to assess the performance of the prediction models, and feature importance was computed via permutation-shuff ling. The H2O AutoML framework had the highest median AUC of 0.846, followed by AutoGluon (median AUC: 0.840) and Autosklearn (median AUC: 0.820), and the medi an training time was the lowest for H2O AutoML (0.14 min), followed by AutoGluon (0.16 min) and Auto-sklearn (4.33 min). Among different types of machine learni ng models, the Gradient Boosting Machines (GBM) or Extreme Gradient Boosting (XG Boost), stacked ensemble, and random forrest models had better predictive perfor mance, with median AUC scores being 0.846, 0.846, and 0.842, respectively. Impor tant features related to preterm birth included premature rupture of membrane (P ROM), incompetent cervix, occupation, and preeclampsia."

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Riphah International University Researchers Update Current Study Findings on Rob otics (Honeycomb Rhombic Torus Vertex-Edge Based Resolvability Parameters and It s Application in Robot Navigation)

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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 Lahore, Pakistan, by NewsRx correspon dents, research stated, "In the aircraft sector, honeycomb composite materials a re frequently employed." The news journalists obtained a quote from the research from Riphah Internationa l University: "Recent research has demonstrated the benefits of honeycomb struct ures in applications involving nanohole arrays in anodized alumina, micro-porous arrays in polymer thin films, activated carbon honeycombs, and photonic band ga p honeycomb structures. The resolvability parameter is the area of graph theory that is most commonly explored. This results in an original network reconfigurat ion. Occasionally in terms of atoms (metric dimension), and sometimes in terms o f bounds (edge metric dimension). In this article, we examined the honeycomb rho mbic torus's metric, edge metric, mixed metric, and partition dimension." According to the news reporters, the research concluded: "The application of edg e metric dimension is also discussed in it."

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Study Data from University of California Los Angeles (UCLA) Update Knowledge of Machine Learning (Probing Historical Image Contexts: Enhancing Visual Archive Re trieval Through Computer Vision)

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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 Learning are pre sented in a new report. According to news reporting originating in Los Angeles, California, by NewsRx journalists, research stated, "This study examines the lon gstanding need and challenge of providing contextual analysis of historical imag es stored in digital visual archives and the accessibility of retrieving context ual information from these historical archives. Contextual analysis is essential for disciplines such as history and art history, as it allows for the contextua lization of artwork and historical sources with historical narratives, which, in turn, enhances understanding of the artistic or political expression in the con tents of cultural products." The news reporters obtained a quote from the research from the University of Cal ifornia Los Angeles (UCLA), "To address this challenge, a novel approach is prop osed utilizing computer vision to trace the circulation and dissemination of his torical photographs in their original contexts. This method involves first using YOLO v7 to crop historical images from pictorial magazines, then training machi ne learning models on the cropped printed images plus another large dataset of o riginal historical photographs, and comparing the similarity of images between t he datasets of printed images and original photographs. To ensure accuracy of im age similarities between the two subsets with distinct image qualities, an ensem ble of three machine learning models-Vision Transformer, EfficientNetv2, and Swi n Transformerwas developed. Through this system, contexts in the circulation of historical photographs were discovered and new insights regarding the editing st rategies of propaganda magazines in East Asia during WWII were uncovered. These outcomes offer supporting evidence for previous research in the history and art historical disciplines, and demonstrate the potential of computer vision for unc overing new information from digital visual archives."

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First Affiliated Hospital of Shenzhen University Reports Findings in Bioinformat ics (Identification of novel biomarkers and immune infiltration characteristics of ischemic stroke based on comprehensive bioinformatic analysis and machine lea rning)

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查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Biotechnology - Bioinf ormatics is the subject of a report. According to news reporting originating in Shenzhen, People's Republic of China, by NewsRx journalists, research stated, "I schemic stroke (IS) is one of most common causes of disability in adults worldwi de. However, there is still a lack of effective and reliable diagnostic markers and therapeutic targets in IS." The news reporters obtained a quote from the research from the First Affiliated Hospital of Shenzhen University, "Furthermore, immune cell dysfunction plays an important role in the pathogenesis of IS. Hence, in-depth research on immune-rel ated targets in progressive IS is urgently needed. Expression profile data from patients with IS were downloaded from the Gene Expression Omnibus (GEO) database . Then, differential expression analysis and weighted gene coexpression network analysis (WGCNA) were performed to identify the significant modules and differen tially expressed genes (DEGs). Key genes were obtained and used in functional en richment analyses by overlapping module genes and DEGs. Next, hub candidate gene s were identified by utilizing three machine learning algorithms: least absolute shrinkage and selection operator (LASSO), random forest, and support vector mac hine-recursive feature elimination (SVM-RFE). Subsequently, a diagnostic model w as constructed based on the hub genes, and receiver operating characteristic (RO C) curves were constructed to validate the performances of the predictive models and candidate genes. Finally, the immune cell infiltration landscape of IS was explored with the CIBERSORT deconvolution algorithm. A total of 40 key DEGs were identified based on the intersection of the DEGs and module genes, and we found that these genes were mainly enriched in the regulation of lipolysis in adipocy tes, neutrophil extracellular trap formation and complement and coagulation casc ades. Based on the results from three advanced machine learning algorithms, we o btained 7 hub candidate genes (ABCA1, ARG1, C5AR1, CKAP4, HMFN0839, SDCBP and TL N1) as diagnostic biomarkers of IS and developed a reliable nomogram with high p redictive performance (AUC = 0.987). In addition, immune cell infiltration dysre gulation was implicated in IS, and compared with those in the normal group, IS p atients had increased fractions of gamma delta T cells, monocytes, M0 macrophage s, M2 macrophages and neutrophils and clearly lower percentages of naive B cells , CD8 T cells, CD4 memory T cells, follicular helper T cells, regulatory T cells (Tregs) and resting dendritic cells. Furthermore, correlation analysis indicate d a significant correlation between the hub genes and immune cells in progressiv e IS. In conclusion, our study identified 7 hub genes as diagnostic biomarkers a nd established a reliable model to predict the occurrence of IS. Meanwhile, we e xplored the immune cell infiltration pattern and investigated the relationship b etween candidate genes and immune cells in the pathogenesis of IS."

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Research Conducted at University of Southern California (USC) Has Updated Our Kn owledge about Machine Learning (Stress Appraisal In the Workplace and Its Associ ations With Productivity and Mood: Insights From a Multimodal Machine Learning . ..)

8-9页

查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Data detailed on Machine Learning have been presented. According to news reporting from Los Angeles, California, by Ne wsRx journalists, research stated, "Previous studies have primarily focused on p redicting stress arousal, encompassing physiological, behavioral, and psychologi cal responses to stressors, while neglecting the examination of stress appraisal . Stress appraisal involves the cognitive evaluation of a situation as stressful or non-stressful, and as a threat/pressure or a challenge/opportunity." Funders for this research include National Science Foundation, National Science Foundation, Army Research Office, Pilot Project Research Training Program of the Southern California NIOSH Education and Research Center. The news correspondents obtained a quote from the research from the University o f Southern California (USC), "In this study, we investigated several research qu estions related to the association between states of stress appraisal (i.e., bor edom, eustress, coexisting eustress-distress, distress) and various factors such as stress levels, mood, productivity, physiological and behavioral responses, a s well as the most effective ML algorithms and data signals for predicting stres s appraisal. The results support the Yerkes-Dodson law, showing that a moderate stress level is associated with increased productivity and positive mood, while low and high levels of stress are related to decreased productivity and negative mood, with distress overpowering eustress when they coexist. Changes in stress appraisal relative to physiological and behavioral features were examined throug h the lenses of stress arousal, activity engagement, and performance. An XGBOOST model achieved the best prediction accuracies of stress appraisal, reaching 82. 78% when combining physiological and behavioral features and 79.55 % using only the physiological dataset. The small accuracy differe nce of 3% indicates that physiological data alone may be adequate to accurately predict stress appraisal, and the feature importance results ident ified electrodermal activity, skin temperature, and blood volume pulse as the mo st useful physiologic features. Implementing these models within work environmen ts can serve as a foundation for designing workplace policies, practices, and st ress management strategies that prioritize the promotion of eustress while reduc ing distress and boredom."

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Universiti Malaya Reports Findings in Root Resorption (Application of deep learn ing and feature selection technique on external root resorption identification o n CBCT images)

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查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Dental Diseases and Co nditions - Root Resorption is the subject of a report. According to news reporti ng from Kuala Lumpur, Malaysia, by NewsRx journalists, research stated, "Artific ial intelligence has been proven to improve the identification of various maxill ofacial lesions. The aim of the current study is two-fold: to assess the perform ance of four deep learning models (DLM) in external root resorption (ERR) identi fication and to assess the effect of combining feature selection technique (FST) with DLM on their ability in ERR identification." Financial support for this research came from Fundamental Research Grant Scheme. The news correspondents obtained a quote from the research from Universiti Malay a, "External root resorption was simulated on 88 extracted premolar teeth using tungsten bur in different depths (0.5 mm, 1 mm, and 2 mm). All teeth were scanne d using a Cone beam CT (Carestream Dental, Atlanta, GA). Afterward, a training ( 70%), validation (10%), and test (20%) da taset were established. The performance of four DLMs including Random Forest (RF ) + Visual Geometry Group 16 (VGG), RF + EfficienNetB4 (EFNET), Support Vector M achine (SVM) + VGG, and SVM + EFNET) and four hybrid models (DLM + FST: (i) FS + RF + VGG, (ii) FS + RF + EFNET, (iii) FS + SVM + VGG and (iv) FS + SVM + EFNET) was compared. Five performance parameters were assessed: classification accurac y, F1-score, precision, specificity, and error rate. FST algorithms (Boruta and Recursive Feature Selection) were combined with the DLMs to assess their perform ance. RF + VGG exhibited the highest performance in identifying ERR, followed by the other tested models. Similarly, FST combined with RF + VGG outperformed oth er models with classification accuracy, F1-score, precision, and specificity of 81.9%, weighted accuracy of 83%, and area under the cu rve (AUC) of 96%. Kruskal Wallis test revealed a significant differ ence (p = 0.008) in the prediction accuracy among the eight DLMs. In general, al l DLMs have similar performance on ERR identification."

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