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    Research from Guangxi University of Science and Technology in the Area of Machin e Learning Published (Current Status of Research on Fault Diagnosis Using Machin e Learning for Gear Transmission Systems)

    58-59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 reporting from Liuzhou, People’s Republic of China, by NewsRx journalists, research stated, “Gear transmission system fault diagnosis is crucial for the reliability and safety of industrial machinery.” Funders for this research include Guangxi Science And Technology Program; Nation al Natural Science Foundation of China; Guangxi Natural Science Foundation. Our news reporters obtained a quote from the research from Guangxi University of Science and Technology: “The combination of mathematical signal processing meth ods with deep learning technology has become a research hotspot in fault diagnos is. Firstly, the development and status of gear transmission system fault diagno sis are outlined in detail. Secondly, the relevant research results on gear tran smission system fault diagnosis are summarized from the perspectives of time-dom ain, frequency domain, and time-frequency-domain analysis.”
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    Selcuk University School of Medicine Reports Findings in Machine Learning (Navig ating the gray zone: Machine learning can differentiate malignancy in PI-RADS 3 lesions)

    59-60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 originating from Konya, Turkey, by News Rx correspondents, research stated, “The objective of this study is to predict t he probability of prostate cancer in PI-RADS 3 lesions using machine learning me thods that incorporate clinical and mpMRI parameters. The study included patient s who had PI-RADS 3 lesions detected on mpMRI and underwent fusion biopsy betwee n January 2020 and January 2024.” Our news journalists obtained a quote from the research from the Selcuk Universi ty School of Medicine, “Radiological parameters (Apparent diffusion coefficient (ADC), tumour ADC/contralateral ADC ratio, Ktrans value, periprostatic adipose t issue thickness, lesion size, prostate volume) and clinical parameters (age, bod y mass index, total prostate specific antigen, free PSA, PSA density, systemic i nflammatory index, neutrophil-lymphocyte ratio [NLR] , platelet lymphocyte ratio, lymphocyte monocyte ratio) were documented. The pro bability of prostate cancer prediction in PI-RADS 3 lesions was calculated using 6 different machine-learning models, with the input parameters being the aforem entioned variables. Of the 235 participants in the trial, 61 had malignant fusio n biopsy pathology and 174 had benign pathology. Among 6 different machine learn ing algorithms, the random forest model had the highest accuracy (0.86±0.04; 95% CI 0.85-0.87), F1 score (0.91±0.03; 95% CI 0.91-0.92) and AUC valu e (0.92±0.06; 95% CI 0.88-0.90). In SHAP analysis based on random forest model, tumour ADC, tumour ADC/contralateral ADC ratio and PSA density wer e the 3 most successful parameters in predicting malignancy. On the other hand, systemic inflammatory index and neutrophil lymphocyte ratio showed higher accura cy in predicting malignancy than total PSA, age, free PSA/total PSA and lesion s ize in SHAP analysis. Among the machine learning models we developed, especially the random forest model can predict malignancy in PI-RADS 3 lesions and prevent unnecessary biopsy.”
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    Study Results from Northeastern University in the Area of Robotics Reported (A N ovel Robot Path Planning Algorithm Based On the Improved Wild Horse Optimiser Wi th Hybrid Strategies)

    60-61页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Fresh data on Robotics are presented in a new rep ort. According to news reporting originating from Shenyang, People’s Republic of China, by NewsRx correspondents, research stated, “Metaheuristic algorithms pla y a pivotal role in addressing the challenges of robot path planning, offering v ersatile, and efficient solutions. Nevertheless, the standard wild horse optimis er (WHO) has limitations, including limited population diversity during initiali sation, constrained global search capability, and challenges in escaping local o ptima.” Our news editors obtained a quote from the research from Northeastern University , “This paper proposed an improved WHO with hybrid strategies (HI-WHO) to overco me these disadvantages in solving robot path planning problem. The algorithm emp loys Sobol sequence for uniform population initialisation, integrating the Le <acute accent >vy flight strategy, and dynamic adaptive f actor to balance exploration and exploitation. Concurrently, it ensures global s earch capability and prevents local optima by using the lens imaging opposition- based learning strategy and greedy mechanism. The robustness and effectiveness of the enhanced algorithm were evaluated on a set of 20 benchmark functions.”
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    Study Findings from University of Manchester Provide New Insights into Robotics and Artificial Intelligence (Towards a computational model for higher orders of Theory of Mind in social agents)

    61-61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 robotics and artificial intelligence have been published. According to news reporting from Ma nchester, United Kingdom, by NewsRx journalists, research stated, “Effective com munication between humans and machines requires artificial tools to adopt a huma n-like social perspective.” The news reporters obtained a quote from the research from University of Manches ter: “The Theory of Mind (ToM) enables understanding and predicting mental state s and behaviours, crucial for social interactions from childhood through adultho od. Artificial agents with ToM skills can better coordinate actions, such as in warehouses or healthcare. Incorporating ToM in AI systems can revolutionise our interactions with intelligent machines.”
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    Royal Surrey County Hospital Reports Findings in Prostatectomy (Predictors of bi ochemical recurrence after robot-assisted radical prostatectomy: single-centre a nalysis)

    61-62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 Surgery - Prostatectom y is the subject of a report. According to news reporting originating in Surrey, United Kingdom, by NewsRx journalists, research stated, “We evaluated risk fact ors for biochemical recurrence (BCR) after robot-assisted radical prostatectomy (RARP) based on our department database. Patients who underwent RARP between 201 8 and 2020 were identified and included in our retrospective study.” The news reporters obtained a quote from the research from Royal Surrey County H ospital, “Patients who received neoadjuvant treatment, patients with positive ly mph nodes, salvage prostatectomies, and patients with missing data were excluded . BCR was defined as PSA 0.2 ng/ml. Parameters that were investigated were the I nternational Society of Urological Pathologists (ISUP) score, stage, and positiv e surgical margins (PSM) as they were reported in the pathology report. A subgro up analysis based on the tumour stage was performed. A total of 414 patients wer e included in the analysis. Seventy-seven of them experienced BCR. Based on mult ivariable analysis, ISUP grade was a strong predictor for BCR with odds ratio (O R): 2.86 (CI: 1.49-5.65; p = 0.002), OR: 5.90 (CI: 1.81-18.6; p = 0.003), OR: 4. 63 (CI: 1.79-11.9; p = 0.001) for ISUP grade 3, 4, 5, respectively. Regarding tu mour stage, pT2 and pT3a did not show any significant difference in predicting B CR (p = 0.11), whereas pT3b stage was a predictor for BCR with OR: 6.2 (CI: 2.25 -17.7; p<0.001). In the subgroup analysis for 206 patients with pT2 disease, ISUP group and PSM were predictors for BCR. On the other hand , when patients with pT3 disease were inspected, the only parameter that was pre dictive of BCR was pT3b disease (OR: 4.68, CI: 1.71-13.6; p = 0.003). ISUP grade , the extent of T3 disease, and the extent and ISUP grade of surgical margins we re not predictors of BCR. The most important risk factors for BCR after RARP are ISUP grade and tumour stage. In pT2 disease, PSM is a significant predictor of BCR, along with high ISUP grade.”
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    Findings from Massachusetts Institute of Technology Provide New Insights into Ma chine Learning (A Multi-Objective Framework for Balancing Fairness and Accuracy in Debiasing Machine Learning Models)

    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 study results on artificial intell igence have been published. According to news reporting originating from Cambrid ge, Massachusetts, by NewsRx correspondents, research stated, “Machine learning algorithms significantly impact decision-making in high-stakes domains, necessit ating a balance between fairness and accuracy.”Funders for this research include Machine Learning Applications Consortium of Mi t Csail; Future of Data Consortium.
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    Curtin University Researcher Discusses Findings in Machine Learning (The future of underground mine planning in the era of machine learning: Opportunities for e ngineering robustness and flexibility)

    63-64页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Researchers detail new data in artificial intelli gence. According to news reporting from Kalgoorlie, Australia, by NewsRx journal ists, research stated, “Machine learning (ML) applications are increasing their footprint in underground mine planning, enabled by the gradual enrichment of res earch methods.” The news journalists obtained a quote from the research from Curtin University: “Indeed, improvements in prediction results have been accelerated in areas such as mining dilution, stope stability, ore grade, and equipment availability, amon g others. In addition, the increasing deployment of equipment with digital techn ologies and rapid information retrieval sensor networks is resulting in the prod uction of immense quantities of operational data. However, despite these favoura ble developments, optimisation studies on key input activities are still siloed, with minimal or no synergies towards the primary objective of optimising the pr oduction schedule. As such, the full potential of ML benefits is not realised. T o explore the potential benefits, this study outlines primary input areas in pro duction scheduling for reference and limits the scope to six key areas, covering dilution prediction, ore grade variability, geotechnical stability, ventilation , mineral commodity prices and data management.”
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    Ningbo Polytechnic Researcher Describes Advances in Support Vector Machines (Ris k Warning Method of Corn Cross-border Supply Chain Based on DBN-MFSVM)

    64-65页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Data detailed on have been presented. According t o news reporting out of Zhejiang, People’s Republic of China, by NewsRx editors, research stated, “There is a large amount of unstructured data in the current c orn cross-border supply chain system and it has the characteristics of multi-sou rce heterogeneous.” Our news journalists obtained a quote from the research from Ningbo Polytechnic: “Traditional risk early warning methods have defects such as over-reliance on m anual decision-making and low accuracy of early warning. In order to solve the a bove problems, this paper proposed a system risk early warning method of corn cr oss-border supply chain based on deep belief network and multi-class fuzzy suppo rt vector machine. Firstly, based on the principle of embedding coding and norma lization, a large number of unstructured data in the corn cross-border supply ch ain system were preprocessed and converted into structured data for subsequent c alculation. Then, based on the deep belief network, the high-latitude features o f the data were extracted, and the change trend and correlation of risk indicato rs in the corn cross-border supply chain system were adaptively mined. Finally, the extracted high-dimensional features were input into the multi-class fuzzy su pport vector machine model for training to realize the risk classification early warning of corn cross-border supply chain. The accuracy of the algorithm propos ed in this paper can reach 94.88% under the condition of similar r unning time.”
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    Investigators from Technical University Munich (TU Munich) Zero in on Robotics a nd Automation (Boxgrounder: 3d Visual Grounding Using Object Size Estimates)

    65-66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 Robotic s - Robotics and Automation. According to news reporting out of Munich, Germany, by NewsRx editors, research stated, “Recent advances in simultaneous localizati on and mapping (SLAM) systems have significantly enhanced the process of creatin g 3D digital replicas of real-world environments. Numerous applications utilizin g these digital twins generally necessitate object-level annotations, which are challenging to acquire.” Financial support for this research came from Federal Ministry of Education & Research (BMBF).
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    Findings from National Institute of Scientific Research Broaden Understanding of Machine Learning (Characterizing Seismic Activity From a Rock Cliff With Unsupe rvised Learning)

    66-67页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators discuss new findings in Machine Learning. According to news reporting originating in Quebec City, Canada , by NewsRx journalists, research stated, “Passive seismic monitoring (PSM) is e merging as a tool for detecting rockfall events and pre-failure seismicity. In t his paper, the potential of PSM for rockfall monitoring is assessed through a ca se study carried out in Gros-Morne, Eastern Qu & eacute;bec, in a region with prominent roadside cliffs, where more than 500 fallen rocks are foun d on the main regional road each year.” Financial supporters for this research include Ministere des Transports du Quebe c, CGIAR, Fonds de recherche du Quebec (FRQ).
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