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    Findings on Machine Learning Discussed by Investigators at University of Catania (Digital Twin Model With Machine Learning and Optimization for Resilient Produc tion-distribution Systems Under Disruptions)

    48-49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 originating from Catania, Italy, by NewsRx c orrespondents, research stated, "Inspired by a real-life problem in the semicond uctor industry, we introduce a novel digital twin model for a company subject to the adverse effects of unpredictable disruptions. Specifically, this company ma nufactures a product using a raw material provided by an external supplier, whos e lead times may abruptly change due to disruptive events." Funders for this research include University of Catania, European Commission Joi nt Research Centre, Spanish Government. Our news journalists obtained a quote from the research from the University of C atania, "The Smoothing Order-Up-To rule is adopted by the company as a replenish ment policy. It is characterized by three control parameters, which must be opti mized to enhance the resilience of the system. To this end, the digital twin lea rns from the real production-distribution data and periodically self-adjusts the replenishment parameters based on the evolution of the external environment. Th e digital twin architecture combines data analytics, simulation modeling, machin e learning, and a metaheuristic. More specifically, an Artificial Neural Network learns from the manufacturer's operations and generates predictive models. Thes e are embedded in a Particle Swarm Optimization, which provides the optimal comb ination of the replenishment parameters. An experimental campaign was performed to demonstrate that the digital twin outperforms the traditional strategy in whi ch the replenishment parameters are kept unchanged."
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    Studies from Aristotle University of Thessaloniki in the Area of Machine Learnin g Reported (An Integrated Machine Learning and Metaheuristic Approach for Advanc ed Packed Bed Latent Heat Storage System Design and Optimization)

    49-50页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-A new study on Machine Learning is now available. According to news reporting originating in Thessaloniki, Greece, by NewsRx jour nalists, research stated, "To tackle the challenge of waste heat recovery in the industrial sector, this research presents a novel design and optimization frame work for Packed Bed Latent Heat Storage Systems (PBLHS). This features a Deep Le arning (DL) model, integrated with metaheuristic algorithms." Financial support for this research came from European Commission Joint Research Centre. The news reporters obtained a quote from the research from the Aristotle Univers ity of Thessaloniki, "The DL model was developed to predict PBLHS performance, t rained using data generated from a validated Computational Fluid Dynamics (CFD) model. The model exhibited a high performance with an R(2 )value of 0.975 and a low Mean Absolute Percentage Error (<9.14%). T o enhance the ML model's efficiency and optimized performance, various metaheuri stic algorithms were explored. The Harmony Search algorithm emerged as the most effective through an early screening and underwent further refinement. The optim ized algorithm demonstrated its capability by rapidly producing designs that sho wcased an improvement in total efficiency of up to 85% over availa ble optimized experimental PBLHS designs."
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    Findings from Wadia Institute of Himalayan Geology Update Understanding of Machi ne Learning (Missing Log Prediction Using Machine Learning Perspectives: a Case Study From Upper Assam Basin)

    50-51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 Uttrakhand, India, by Ne wsRx journalists, research stated, "In the field of reservoir characterization a nd management, the completeness and accuracy of geophysical logs are pivotal. Of ten, these logs are marred by missing segments or distortions due to logistical and environmental challenges in boreholes." Financial support for this research came from Science Engineering Research Board (SERB), India. The news correspondents obtained a quote from the research from the Wadia Instit ute of Himalayan Geology, "To address this issue, our study introduces synergist ic method combining log data preconditioning with advanced machine learning (ML) techniques-including k-nearest neighbors, support vector machine, decision tree , random forest, extreme gradient boosting, gaussian process regression, and art ificial neural networks. We focus on uncovering the complex, nonlinear relations hips inherent in geophysical logs through a robust analysis involving a correlat ion matrix and F-test for predictor significance and ranking. We applied this ap proach to the wireline logs of the Lakadong-Therria Formation in the Bhogpara oi l field, India, to demonstrate the effectiveness of ML in reliably predicting mi ssing logs. Notably, our ML models adeptly forecast bulk density logs using data from gamma-ray, deep resistivity, neutron porosity, and photoelectric factor lo gs. The high R-squared correlation coefficients achieved (R2 score: over 0.89 in training and 0.85 in testing phases) attest to the accuracy of our predictions. "
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    Studies from University of Stuttgart Add New Findings in the Area of Machine Lea rning (Towards Efficient Powder Quality Control In Additive Manufacturing Via an In Situ Capable Device and Methodology Leveraging Multispectral Machine Learnin g)

    51-52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 out of Stuttgart, Germany, by NewsRx editors, research stated, "Additive manufacturing (AM) processes enabl es the fabrication of highly complex parts that cannot be manufactured using con ventional manufacturing methods. Constant and specified material properties are of crucial importance for these highly optimized components." Financial support for this research came from German Federal Ministry for Econom ic Affairs and Energy - University of Stuttgart in a technology transfer project fund.
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    Zhejiang Laboratory Reports Findings in Machine Learning (MLatom Software Ecosys tem for Surface Hopping Dynamics in Python with Quantum Mechanical and Machine L earning Methods)

    52-53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 Zhejiang, People's Repub lic of China, by NewsRx journalists, research stated, "We present an open-source MLatom@XACS software ecosystem for on-the-fly surface hopping nonadiabatic dyna mics based on the Landau-Zener-Belyaev-Lebedev algorithm. The dynamics can be pe rformed via Python API with a wide range of quantum mechanical (QM) and machine learning (ML) methods, including ab initio QM (CASSCF and ADC(2)), semiempirical QM methods (e.g., AM1, PM3, OMx, and ODMx), and many types of ML potentials (e. g., KREG, ANI, and MACE)."
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    Reports Outline Robotics Findings from Zhejiang University (Motion Behavior of a 30-strut Locomotive Tensegrity Robot)

    53-54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 have been published. According to news originating from Zhejiang, People's Republic of China, by NewsRx correspondents, research stated, "Tensegrity structure is a prestressed self-equilibrated system consisting of compressed struts and tension ed tendons. The shape and position of tensegrity can be actively controlled by c hanging the lengths of members, making it attractive as a platform for adaptive bionic and locomotive robots." Financial supporters for this research include National Natural Science Foundati on of China (NSFC), Natural Science Foundation of Zhejiang Province.
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    Guangzhou University of Chinese Medicine Reports Findings in Osteoporosis (Machi ne-learning models for diagnosis of rotator cuff tears in osteoporosis patients based on anteroposterior X-rays of the shoulder joint)

    54-55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 Musculoskeletal Diseas es and Conditions - Osteoporosis is the subject of a report. According to news r eporting from Shenzhen, People's Republic of China, by NewsRx journalists, resea rch stated, "This study aims to diagnose Rotator Cuff Tears (RCT) and classify t he severity of RCT in patients with Osteoporosis (OP) through the analysis of sh oulder joint anteroposterior (AP) X-ray-based localized proximal humeral bone mi neral density (BMD) measurements and clinical information based on machine learn ing (ML) models. A retrospective cohort of 89 patients was analyzed, including 6 3 with both OP and RCT (OPRCT) and 26 with OP only." The news correspondents obtained a quote from the research from the Guangzhou Un iversity of Chinese Medicine, "The study analyzed a series of shoulder radiograp hs from April 2021 to April 2023. Grayscale values were measured after plotting ROIs based on AP X-rays of shoulder joint. Five kinds of ML models were develope d and compared based on their performance in predicting the occurrence and sever ity of RCT from ROIs' greyscale values and clinical information (age, gender, ad vantage side, lumbar BMD, and acromion morphology (AM)). Further analysis using SHAP values illustrated the significant impact of selected features on model pre dictions. R1-6 had a positive correlation with BMD respectively. The nine variab les, including greyscale R1-6, age, BMD, and AM, were used in the prediction mod els. The RF model was determined to be superior in effectively diagnosing RCT in OP patients, with high AUC scores of 0.998, 0.889, and 0.95 in the training, va lidation, and testing sets, respectively. SHAP values revealed that the most inf luential factors on the diagnostic outcomes were the grayscale values of all can cellous bones in ROIs. A column-line graph prediction model based on nine variab les was constructed, and DCA curves indicated that RCT prediction in OP patients was favored based on this model. Furthermore, the RF model was also the most su perior in predicting the types of RCT within the OPRCT group, with an accuracy o f 86.364% and 73.684% in the training and test sets, respectively. SHAP values indicated that the most significant factor affecting the predictive outcomes was the AM, followed by the grayscale values of the grea ter tubercle, among others. ML models, particularly the RF algorithm, show signi ficant promise in diagnosing RCT occurrence and severity in OP patients using co nventional shoulder X-rays based on the nine variables."
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    Studies from Shandong Normal University Provide New Data on Machine Learning (En hanced prediction of cement raw meal oxides by near-infrared spectroscopy using machine learning combined with chemometric techniques)

    55-56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 Jinan, People's Republic of C hina, by NewsRx journalists, research stated, "The component analysis of raw mea l is critical to the quality of cement." The news correspondents obtained a quote from the research from Shandong Normal University: "In recent years, near-infrared (NIR) has been emerged as an innovat ive and efficient analytical method to determine the oxide content of cement raw meal. This study aims to utilize NIR spectroscopy combined with machine learnin g and chemometrics to improve the prediction of oxide content in cement raw meal . The Savitzky-Golay convolution smoothing method is applied to eliminate noise interference for the analysis of calcium carbonate (CaCO3), silicon dioxide (SiO 2), aluminum oxide (Al2O3), and ferric oxide (Fe2O3) in cement raw materials. Di fferent wavelength selection techniques are used to perform a comprehensive anal ysis of the model, comparing the performance of several wavelength selection tec hniques. The back-propagation neural network regression model based on particle swarm optimization algorithm was also applied to optimize the extracted and scre ened feature wavelengths, and the model prediction performance was checked and e valuated using Rp and RMSE. In conclusion, the results indicate that NIR spectro scopy in combination with ML and chemometrics has great potential to effectively improve the prediction performance of oxide content in raw materials and highli ght the importance of modeling and wavelength selection techniques."
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    University of Porto Reports Findings in Hernias (Robotic surgery versus Laparosc opic surgery for anti-reflux and hiatal hernia surgery: a short-term outcomes an d cost systematic literature review and meta-analysis)

    56-57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 Gastroenterology - Her nias is the subject of a report. According to news reporting out of Porto, Portu gal, by NewsRx editors, research stated, "The objective of this study is to comp are the operative time, intraoperative complications, length of stay, readmissio n rates, overall complications, mortality, and cost associated with Robotic Surg ery (RS) and Laparascopic Surgery (LS) in anti-reflux and hiatal hernia surgery. A comprehensive literature search was conducted using MEDLINE (via PubMed), Web of Science and Scopus databases." Our news journalists obtained a quote from the research from the University of P orto, "Studies comparing short-term outcomes and cost between RS and LS in patie nts with anti-reflux and hiatal hernia were included. Data on operative time, co mplications, length of stay, readmission rates, overall complications, mortality , and cost were extracted. Quality assessment of the included studies was perfor med using the MINORS scale. Fourteen retrospective observational studies involvi ng a total of 555,368 participants were included in the meta-analysis. The resul ts showed no statistically significant difference in operative time, intraoperat ive complications, length of stay, readmission rates, overall complications, and mortality between RS and LS. However, LS was associated with lower costs compar ed to RS. This systematic review and meta-analysis demonstrates that RS has non- inferior short-term outcomes in antireflux and hiatal hernia surgery, compared to LS. LS is more cost-effective, but RS offers potential benefits such as impro ved visualization and enhanced surgical techniques."
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    Reports Summarize Machine Learning Study Results from Hebei University of Techno logy [Experimental and Numerical Investigation Integrated wit h Machine Learning (ML) for the Prediction Strategy of DP590/CFRP Composite Lami nates]

    57-58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 artificial intelligence. According to news reporting originating from Tianjin, P eople's Republic of China, by NewsRx correspondents, research stated, "This stud y unveils a machine learning (ML)-assisted framework designed to optimize the st acking sequence and orientation of carbon fiber-reinforced polymer (CFRP)/metal composite laminates, aiming to enhance their mechanical properties under quasi-s tatic loading conditions." Financial supporters for this research include Science & Technolog y Development Fund of Tianjin Education Commission For Higher Education.
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