期刊,Robotics & Machine Learning Daily News 2024年卷Jun.19期_国家学术搜索

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University of Auckland Reports Findings in Brain Injury (Classification of short and long term mild traumatic brain injury using computerized eye tracking)

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 research on Central Nervous System Diseases and Conditions - Brain Injury is the subject of a report. According to news reporting originating in Auckland, New Zealand, by NewsRx journalists, res earch stated, "Accurate, and objective diagnosis of brain injury remains challen ging. This study evaluated useability and reliability of computerized eye-tracke r assessments (CEAs) designed to assess oculomotor function, visual attention/pr ocessing, and selective attention in recent mild traumatic brain injury (mTBI), persistent post-concussion syndrome (PPCS), and controls." The news reporters obtained a quote from the research from the University of Auc kland, "Tests included egocentric localisation, fixation-stability, smooth-pursu it, saccades, Stroop, and the vestibulo-ocular reflex (VOR). Thirty-five healthy adults performed the CEA battery twice to assess useability and test-retest rel iability. In separate experiments, CEA data from 55 healthy, 20 mTBI, and 40 PPC S adults were used to train a machine learning model to categorize participants into control, mTBI, or PPCS classes. Intraclass correlation coefficients demonst rated moderate (ICC > .50) to excellent (ICC > .98) reliability (p <.05) and satisfactory CEA compliance . Machine learning modelling categorizing participants into groups of control, m TBI, and PPCS performed reasonably (balanced accuracy control: 0.83, mTBI: 0.66, and PPCS: 0.76, AUC-ROC: 0.82). Key outcomes were the VOR (gaze stability), fix ation (vertical error), and pursuit (total error, vertical gain, and number of s accades). The CEA battery was reliable and able to differentiate healthy, mTBI, and PPCS patients reasonably well."

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Recent Research from Hohai University Highlight Findings in Machine Learning (Ev aluation and Machine Learning Prediction On Thermal Performance of Energy Walls In Underground Spaces As Part of Ground Source Heat Pump Systems)

77-77页

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 originating in Nanjing, People's Republi c of China, by NewsRx journalists, research stated, "Energy walls (i.e. wall hea t exchangers) exhibit more complex thermal performance than do energy piles and borehole heat exchangers in ground source heat pump systems, due to their indivi dual geometry. This study conducted field tests of energy wall thermal performan ce to analyze the influence and interaction of the pipe burial depth, flow rate, heating load, and operation mode."

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Study Results from University of Brunei Darussalam Provide New Insights into Mac hine Learning (Use of Machine Learning Models In Condition Monitoring of Abrasiv e Belt In Robotic Arm Grinding Process)

78-79页

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 originating from Gadong, Brunei, by NewsRx corresp ondents, research stated, "Although the aspects that affect the performance and the deterioration of abrasive belt grinding are known, wear prediction of abrasi ve belts in the robotic arm grinding process is still challenging. Massive wear of coarse grains on the belt surface has a serious impact on the integrity of th e tool and it reduces the surface quality of the finished products." Financial support for this research came from Universiti Brunei Darussalam. Our news journalists obtained a quote from the research from the University of B runei Darussalam, "Conventional wear status monitoring strategies that use speci al tools result in the cessation of the manufacturing production process which s ometimes takes a long time and is highly dependent on human capabilities. The er ratic wear behavior of abrasive belts demands machining processes in the manufac turing industry to be equipped with intelligent decision-making methods. In this study, to maintain a uniform tool movement, an abrasive belt grinding is instal led at the end-effector of a robotic arm to grind the surface of a mild steel wo rkpiece. Simultaneously, accelerometers and force sensors are integrated into th e system to record its vibration and forces in real-time. The vibration signal r esponses from the workpiece and the tool reflect the wear level of the grinding belt to monitor the tool's condition. Intelligent monitoring of abrasive belt gr inding conditions using several machine learning algorithms that include KNeare st Neighbor (KNN), Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), a nd Decision Tree (DT) are investigated. The machine learning models with the opt imized hyperparameters that produce the highest average test accuracy were found using the DT, Random Forest (RF), and XGBoost. Meanwhile, the lowest latency wa s obtained by DT and RF. A decision-tree-based classifier could be a promising m odel to tackle the problem of abrasive belt grinding prediction."

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Oxford University Reports Findings in Machine Learning (Glass Box and Black Box Machine Learning Approaches to Exploit Compositional Descriptors of Molecules in Drug Discovery and Aid the Medicinal Chemist)

79-79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 Oxford, United Kingdom, by NewsRx journalists, research stated, "The synthetic medicinal chemi st plays a vital role in drug discovery. Today there are AI tools to guide next syntheses, but many are ‘Black Boxes' (BB)." The news reporters obtained a quote from the research from Oxford University, "O ne learns little more than the prediction made. There are now also AI methods em phasizing visibility and ‘explainability' (thus explainable AI or XAI) that coul d help when ‘compositional data' are used, but they often still start from seemi ngly arbitrary learned weights and lack familiar probabilistic measures based on observation and counting from the outset. If probabilistic methods were used in a complementary way with BB methods and demonstrated comparable predictive powe r, they would provide guidelines about what groups to include and avoid in next syntheses and quantify the relationships in probabilistic terms. These points ar e demonstrated by blind test comparison of two main types of BB methods and a pr obabilistic ‘Glass Box' (GB) method new outside of medicine, but which appears w ell suited to the above. Because many probabilities can be involved, emphasis is on the predictive power of its simplest explanatory models. There are usually m ore inactive compounds by orders of magnitude, often a problem for machine learn ing methods."

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Research from Szechenyi Istvan University Provides New Study Findings on Robotic s (Weed Detection and Classification with Computer Vision Using a Limited Image Dataset)

80-80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 robotics are presented i n a new report. According to news reporting originating from Mosonmagyarovar, Hu ngary, by NewsRx correspondents, research stated, "In agriculture, as precision farming increasingly employs robots to monitor crops, the use of weeding and har vesting robots is expanding the need for computer vision." The news correspondents obtained a quote from the research from Szechenyi Istvan University: "Currently, most researchers and companies address these computer v ision tasks with CNN-based deep learning. This technology requires large dataset s of plant and weed images labeled by experts, as well as substantial computatio nal resources. However, traditional feature-based approaches to computer vision can extract meaningful parameters and achieve comparably good classification res ults with only a tenth of the dataset size. This study presents these methods an d seeks to determine the minimum number of training images required to achieve r eliable classification. We tested the classification results with 5, 10, 20, 40, 80, and 160 images per weed type in a four-class classification system. We extr acted shape features, distance transformation features, color histograms, and te xture features."

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Qilu Hospital of Shandong University Reports Findings in Pancreatic Cancer (Prel iminary study on the ability of the machine learning models based on 18F-FDG PET /CT to differentiate between massforming pancreatic lymphoma and pancreatic ... )

81-82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 Oncology - Pancreatic Cancer is the subject of a report. According to news reporting originating from Jinan, People's Republic of China, by NewsRx correspondents, research stated, "T he objective of this study was to preliminarily assess the ability of metabolic parameters and radiomics derived from F-fluorodeoxyglucose positron emission tom ography/computed tomography (F-FDG PET/CT) to distinguish mass-forming pancreati c lymphoma from pancreatic carcinoma using machine learning. A total of 88 lesio ns from 86 patients diagnosed as mass-forming pancreatic lymphoma or pancreatic carcinoma were included and randomly divided into a training set and a validatio n set at a 4-to-1 ratio." Our news editors obtained a quote from the research from the Qilu Hospital of Sh andong University, "The segmentation of regions of interest was performed using ITK-SNAP software, PET metabolic parameters and radiomics features were extracte d using 3Dslicer and PYTHON. Following the selection of optimal metabolic parame ters and radiomics features, Logistic regression (LR), support vector machine (S VM), and random forest (RF) models were constructed for PET metabolic parameters , CT radiomics, PET radiomics, and PET/CT radiomics. Model performance was asses sed in terms of area under the curve (AUC), accuracy, sensitivity, and specifici ty in both the training and validation sets. Strong discriminative ability obser ved in all models, with AUC values ranging from 0.727 to 0.978. The highest perf ormance exhibited by the combined PET and CT radiomics features. AUC values for PET/CT radiomics models in the training set were LR 0.994, SVM 0.994, RF 0.989. In the validation set, AUC values were LR 0.909, SVM 0.883, RF 0.844. Machine le arning models utilizing the metabolic parameters and radiomics of F-FDG PET/CT s how promise in distinguishing between pancreatic carcinoma and mass-forming panc reatic lymphoma."

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Affiliated Hospital of Nanjing University of Chinese Medicine Reports Findings i n Rectal Cancer (Comparing short-term outcomes of robot-assisted and conventiona l laparoscopic total mesorectal excision surgery for rectal cancer in elderly .. .)

82-83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 Oncology - Rectal Canc er is the subject of a report. According to news reporting out of Nanjing, Peopl e's Republic of China, by NewsRx editors, research stated, "Da Vinci Robotics-as sisted total mesorectal excision (TME) surgery for rectal cancer is becoming mor e widely used. There is no strong evidence that robotic-assisted surgery and lap aroscopic surgery have similar outcomes in elderly patients with TME for rectal cancer." Our news journalists obtained a quote from the research from the Affiliated Hosp ital of Nanjing University of Chinese Medicine, "To determine the improved oncol ogical outcomes and short-term efficacy of robot-assisted surgery in elderly pat ients undergoing TME surgery. A retrospective study of the clinical pathology an d follow-up of elderly patients who underwent TME surgery at the Department of G astrointestinal Oncology at the Affiliated Hospital of Nanjing University of Chi nese Medicine was conducted from March 2020 through September 2023. The patients were divided into a robot-assisted group (the R-TME group) and a laparoscopic g roup (the L-TME group), and the short-term efficacy of the two groups was compar ed. There were 45 elderly patients ( 60 years) in the R-TME group and 50 elderly patients ( 60 years) in the L-TME group. There were no differences in demograph ics, conversion rates, or postoperative complication rates. The L-TME group had a longer surgical time than the R-TME group [145 (125, 187.5) 180 (148.75, 206.25) min, = 0.005), and the first postoperative meal time in th e L-TME group was longer than that in the R-TME (4 3 d, = 0.048). Among the sex and body mass index (BMI) subgroups, the R-TME group had better outcomes than di d the L-TME group in terms of operation time ( = 0.042) and intraoperative asses sment of bleeding ( = 0.042). In the high BMI group, catheter removal occurred e arlier in the R-TME group than in the L-TME group (3 4 d, = 0.001), and autonomo us voiding function was restored. The curative effect and short-term efficacy of robot-assisted TME surgery for elderly patients with rectal cancer are similar to those of laparoscopic TME surgery; however, robotic-assisted surgery has bett er short-term outcomes for individuals with risk factors such as obesity and pel vic stenosis."

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Investigators from University of Washington Release New Data on Artificial Intel ligence [Chat Generative Pretrained Transformer (Chatgpt) and Bard: Artificial Intelligence Does Not yet Provide Clinically Supported Answers for Hip and Knee ...]

83-84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 Artificial Intelligence are presented in a new report. According to news reporting originating in Seattl e, Washington, by NewsRx journalists, research stated, "Advancements in artifici al intelligence (AI) have led to the creation of large language models (LLMs), s uch as Chat Generative Pretrained Transformer (ChatGPT) and Bard, that analyze o nline resources to synthesize responses to user queries. Despite their popularit y, the accuracy of LLM responses to medical questions remains unknown." The news reporters obtained a quote from the research from the University of Was hington, "This study aimed to compare the responses of ChatGPT and Bard regardin g treatments for hip and knee osteoarthritis with the American Academy of Orthop aedic Surgeons (AAOS) Evidence-Based Clinical Practice Guidelines (CPGs) recomme ndations. Both ChatGPT (Open AI) and Bard (Google) were queried regarding 20 tre atments (10 for hip and 10 for knee osteoarthritis) from the AAOS CPGs. Response s were classified by 2 reviewers as being in ‘Concordance, ‘ ‘Discordance, ‘ or ‘No Concordance ‘ with AAOS CPGs. A Cohen ‘s Kappa coefficient was used to asses s inter -rater reliability, and Chi -squared analyses were used to compare respo nses between LLMs. Overall, ChatGPT and Bard provided responses that were concor dant with the AAOS CPGs for 16 (80%) and 12 (60%) trea tments, respectively. Notably, ChatGPT and Bard encouraged the use of nonrecomme nded treatments in 30% and 60% of queries, respectiv ely. There were no differences in performance when evaluating by joint or by rec ommended versus non-recommended treatments. Studies were referenced in 6 (30% ) of the Bard responses and none (0%) of the ChatGPT responses. Of the 6 Bard responses, studies could only be identified for 1 (16.7% ). Of the remaining, 2 (33.3%) responses cited studies in journals that did not exist, 2 (33.3%) cited studies that could not be found with the information given, and 1 (16.7%) provided links to unrela ted studies. Both ChatGPT and Bard do not consistently provide responses that al ign with the AAOS CPGs."

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New Machine Learning Study Findings Has Been Reported by a Researcher at Shangha i University (Improved Machine Learning Model for Urban Tunnel Settlement Predic tion Using Sparse Data)

84-85页

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 artificial intelligence is now available. According to news reporting from Shanghai, People's Republic of China, by NewsRx journalists, research stated, "Prediction tunnel settlement in shield tunnels during the operation period has gained increasing significance within the realm of maintenance strategy formulation." Funders for this research include Science And Technology Commission of Shanghai Municipality; Natural Science Foundation of Shanghai, China.

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New Machine Learning Study Findings Have Been Published by Researchers at Dong-A University (Optimizing Urban-Scale Evacuation Strategies Through Disaster Victi m Aggregation Modification)

85-86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 Busan, South Korea, by NewsRx correspondents, research stated, "As urban area s continue progressing into more complex development, urban areas become more vu lnerable to disaster." Funders for this research include Basic Science Research Program Through The Nat ional Research Foundation; Korean Ministry of Education.

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