期刊,Robotics & Machine Learning Daily News 2024年卷Sep.10期_国家学术搜索

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Study Data from University of Dar es Salaam Update Understanding of Artificial I ntelligence (Technologies To Decontaminate Aflatoxins In Foods: a Review)

75-75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 Dar Es Salaam, Tanzania, by NewsRx journalists, research stated, "Aflatoxins are toxic secondary metabolites produced by Aspergillus spp., found in staple food commod ities. Aflatoxins are carcinogenic, teratogenic, and mutagenic and pose a seriou s threat to the health of humans." Financial support for this research came from University of Dar es Salaam. The news reporters obtained a quote from the research from the University of Dar es Salaam, "The identification and quantification of aflatoxins in foods is a m ajor challenge to guarantee food safety. Therefore, developing feasible, sensiti ve, and robust methods for decontamination is paramount, with short processing t ime and negligible impact on quality. This review evaluates recent novel technol ogies for aflatoxins decontamination by physical methods (microwave heating, Gam a and electron beam irradiation, pulse light and ultraviolet), chemical methods (ozone, natural plant extracts, and organic acids), and biological methods (atox igenic Aspergillus strains, Trichoderma spp., and bacteria and yeast). The study highlights on the cutting-edge technologies of smart packaging and artificial i ntelligence (AI). To achieve more efficiency and adaptability to different food matrices in aflatoxins decontamination, the study suggests integrating multiple strategies. The study also recommends integrating Partnership for Delivery (P4D) to share the responsibility to increase the chance for success and control aflatoxins in foods. This review evaluates recent novel technologies for aflatoxins decontamination by physical method (microwave heating, Gama and electron beam ir radiation, pulse light and ultraviolet), chemical method (ozone, natural plant e xtracts, and organic acids), and biological method (atoxigenic Aspergillus strai ns, Trichoderma spp., and bacteria and yeast)."

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New Research on Machine Learning from Semnan University Summarized (A novel mach ine learning-based model for predicting the transition fatigue lifetime in pisto n aluminum alloys)

76-76页

查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Current study results on artificial intelligence have been published. According to news reporting originating from Semnan, Iran, by NewsRx correspondents, research stated, "The estimation of transition fatigue lifetimes for piston aluminum alloys was carried out using unsupervised machine learning (ML) with the K-means algorithm." Our news editors obtained a quote from the research from Semnan University: "For this purpose, an experimental dataset representing standard ISO specimens with piston aluminum alloy material, which was subjected to rotational bending fatigu e tests under fully reversed cyclic load conditions, was utilized. Subsequently, the stress and fatigue lifetime data were employed to fit the algorithm of K-me ans clustering. Then, to enhance the K-means performance, various preprocessing methods and Kernel functions were employed to cluster fatigue lifetime and stres s data. Furthermore, following the division of the data into multiple clusters, the middle cluster, which represents fatigue lifetime and stress, was identified as the transition fatigue region, and its center defines the estimated transiti on fatigue lifetime. Ultimately, the transition fatigue lifetimes were determine d using the Coffin-Manson-Basquin equation for piston aluminum alloys and compar ed to the estimated transition fatigue lifetimes, along with the calculation of relative errors. The obtained results indicated that, among the different models employed in this study, the polynomial Kernel K-means clustering algorithm prov ed to be the most efficient for clustering data within stress and number of cycl es plots (S-N plots)."

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Research from Ibn Tofail University Provide New Insights into Machine Learning ( A Performance Analysis of Stochastic Processes and Machine Learning Algorithms i n Stock Market Prediction)

77-77页

查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Investigators discuss new findings in artificial intelligence. According to news reporting from Kenitra, Morocco, by NewsRx journ alists, research stated, "In this study, we compare the performance of stochasti c processes, namely, the Vasicek, Cox-Ingersoll-Ross (CIR), and geometric Browni an motion (GBM) models, with that of machine learning algorithms, such as Random Forest, Support Vector Machine (SVM), and k-Nearest Neighbors (KNN), for predic ting the trends of stock indices XLF (financial sector), XLK (technology sector) , and XLV (healthcare sector)." Our news correspondents obtained a quote from the research from Ibn Tofail Unive rsity: "The results showed that stochastic processes achieved remarkable predict ion performance, especially the CIR model. Additionally, this study demonstrated that the metrics of machine learning algorithms are relatively lower. However, it is important to note that stochastic processes use the actual current index v alue to predict tomorrow's value, which may overestimate their performance. In c ontrast, machine learning algorithms offer a more flexible approach and are not as dependent on the current index value."

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Yuebei People's Hospital Reports Findings in Stroke (Effectiveness of the A3 rob ot on lower extremity motor function in stroke patients: A prospective, randomiz ed controlled trial)

77-78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 Cerebrovascular Diseas es and Conditions-Stroke is the subject of a report. According to news reporti ng out of Shaoguan, People's Republic of China, by NewsRx editors, research stat ed, "The results of existing lower extremity robotics studies are conflicting, a nd few relevant clinical trials have examined short-term efficacy. In addition, most of the outcome indicators in existing studies are scales, which are not obj ective enough."

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Queen Mary University of London Reports Findings in Robotics (The Integration of Robotic Arm and Vision System With Magnetic Tractor Beam Control for Precision Catheter Manipulation In Medical Procedures)

78-79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 Robotics is the subjec t of a report. According to news reporting originating in London, United Kingdom , by NewsRx journalists, research stated, "This study addresses the challenges o f traditional catheterization techniques by integrating UFACTORY's uArm Swift Pr o robotic arm with the OpenMV camera module, enhanced by the magnetic tractor be am (MTB) method. The goal is to improve precision, stability, and minimally inva sive operation in catheter-based medical procedures." The news reporters obtained a quote from the research from the Queen Mary Univer sity of London, "The uArm Swift Pro offers a robust and adaptable platform, whil e the OpenMV camera provides accurate real-time tracking of catheter tips. To ev aluate the system's effectiveness, experimental models replicating realistic anatomical scenarios were created using advanced three-dimensional (3D) printing te chniques. Preliminary results demonstrate that this integrated system enhances t he accuracy and safety of catheterization, suggesting its potential to advance m edical robotics and contribute to more patient-friendly interventions."

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Data on Machine Learning Reported by Researchers at Harbin Institute of Technolo gy (Teamwork Culture, Employee Stock Ownership Plan, and Firm Open Innovation: E mpirical Evidence From Novel Measures Based On Machine Learning)

79-80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 Ma chine Learning. According to news reporting originating from Harbin, People's Re public of China, by NewsRx correspondents, research stated, "Recent literature h as acknowledged the role of organizational culture in shaping firm open innovati on outcomes. We contribute to this literature by invoking how firm teamwork cult ure benefits open innovation outcomes for China A-listed firms." Funders for this research include National Natural Science Foundation of China ( NSFC), China Postdoctoral Science Foundation, China Postdoctoral Science Foundat ion, Heilongjiang Province University Think Tank Open Project.

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Reports from Nanjing Xiaozhuang University Add New Datato Findings in Robotics (The Role of Service Robots In Enhancing Customer Satisfaction In Embarrassing C ontexts)

80-81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 reporting originating from Nanjing, People's Republic of China, by NewsRx correspondents, research stated, "The widespread in tegration of service robots in the tourism and hospitality industry has shifted from humantohuman interaction to human-to-robot interaction during service enco unters, thereby affecting customer mood and satisfaction." Funders for this research include National Social Science Fund of China, Innovat ion Project of Guangxi Graduate Educa-tion. Our news editors obtained a quote from the research from Nanjing Xiaozhuang Univ ersity, "In contrast to previous research on service robots' tendency to evoke n egative emotions, this paper employs three scenario experiments to analyze the e ffect of service robots in alleviating social anxiety in embarrassing service co ntext, comparing their effects to those of frontline employees. The study result s indicate that the utilization of a service robot can result in higher levels o f customer satisfaction than interactions with a frontline employee in embarrass ing service contexts, with social anxiety serving as a mediating factor."

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New Findings on Machine Learning Described by Investigators at Queen Mary Univer sity of London (Bank Capital, Liquidity Creation and the Moderating Role of Bank Culture: an Investigation Using a Machine Learning Approach)

81-81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 Machine Learning. According to news reporting out of London, United Kingdom, by NewsRx editors, research stated, "This empirical study investigates whether a strong ba nk culture may help strengthen, weaken, or have no effect on the relationship be tween regulatory capital and liquidity creation. Using a machine learning approa ch and banks' 10-K reports, we first measure the corporate culture of selected b ank holding companies (BHCs) in the United State (U.S.) over the period between 1995 and 2019." Our news journalists obtained a quote from the research from the Queen Mary Univ ersity of London, "We find that bank culture does affect the link between regulatory capital and liquidity creation. In particular, while we find that regulator y capital has a negative impact on bank liquidity creation, a strong culture in a bank weakens this negative association. We also find that an increase in asset -side liquidity creation is the main channel through which bank culture exerts i ts moderating role. Finally, our results are largely driven by smaller banks, ba nks with a more traditional funding structure and more profitable banks."

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Study Results from University of Claude Bernard Lyon 1 in the Area of Machine Le arning Reported (Machine Learning Based Methods for Ratemaking Health Care Insur ance)

82-82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 Machine Learning. According to news reporting out of Villeurbanne, France, by NewsRx ed itors, research stated, "In insurance, proposing an accurate premium that is adj usted to the insured risk profile allows companies to better manage their portfo lios and to be more competitive. Machine learning methods have recently been ado pted for various improvements in insurance ratemaking, especially in the automob ile industry." Our news journalists obtained a quote from the research from the University of C laude Bernard Lyon 1, "These models are specifically used to mine potential data information and to build a predictive model for a variable of interest using ex planatory variables. In this paper, we aim to provide a pricing method for ratem aking individual healthcare insurance contracts using machine learning algorithm s that are applied to atunisian healthcare insurance portfolio. We start with a simple Classification and Regression Tree, and we work toward more advanced met hods that are Random Forest, Extreme gradient boosting, Support Vector Regressio n, and Artificial Neural network regression model. The predictive performance of these non-parametric methods is compared with the standard generalized linear m odel."

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Reports Summarize Machine Learning Study Results from Southeast University (Mach ine Learning Driven Bond Performance Prediction Between Frp Bars and Coral Aggre gate Concrete)

82-83页

查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Investigators discuss new findings in Machine Lea rning. According to news reporting originating from Nanjing, People's Republic o f China, by NewsRx correspondents, research stated, "This study aims to apply ma chine learning (ML) methods to predict the failure mode and bond stress between FRP bars and coral aggregate concrete (CAC). The central pull test dataset of 22 1 FRP bars and CAC was synthesized for training and testing six supervised ML mo dels, including Artificial Neural Network (ANN), Decision Tree, K-Nearest Neighb ors (KNN), Random Forest, Support Vector Machine (SVM) and eXtreme Gradient Boos ting Trees (XGBoost)."

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