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    Federal University of Lavras Reports Findings in Machine Learning (Models for pr edicting coffee yield from chemical characteristics of soil and leaves using mac hine learning)

    86-86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 Lavras,Brazil,by New sRx correspondents,research stated,"Coffee farming constitutes a substantial e conomic resource,representing a source of income for several countries due to t he high consumption of coffee worldwide. Precise management of coffee crops invo lves collecting crop attributes (characteristics of the soil and the plant),map ping,and applying inputs according to the plants' needs." Our news journalists obtained a quote from the research from the Federal Univers ity of Lavras,"This differentiated management is precision coffee growing and i t stands out for its increased yield and sustainability. This research aimed to predict yield in coffee plantations by applying machine learning methodologies t o soil and plant attributes. The data were obtained in a field of 54.6 ha during two consecutive seasons,applying varied fertilization rates in accordance with the recommendations of soil attribute maps. Leaf analysis maps also were monito red with the aim of establishing a correlation between input parameters and yiel d prediction. The machine-learning models obtained from these data predicted cof fee yield efficiently. The best model demonstrated predictive fit results with a Pearson correlation of 0.86. Soil chemical attributes did not interfere with th e prediction models,indicating that this analysis can be dispensed with when ap plying these models." According to the news editors,the research concluded: "These findings have impo rtant implications for optimizing coffee management and cultivation,providing v aluable insights for producers and researchers interested in maximizing yield us ing precision agriculture."
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    New Findings on Machine Learning Described by Investigators at University of Mal aga (Integrating Fmi and Ml/ai Models On the Open-source Digital Twin Framework Opentwins)

    87-87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 Malaga,Spain,by N ewsRx journalists,research stated,"The realm of digital twins is experiencing rapid growth and presents a wealth of opportunities for Industry 4.0. In conjunc tion with traditional simulation methods,digital twins offer a diverse range of possibilities." Financial support for this research came from Ministerio de Ciencias y Universid ades ,Spain. The news reporters obtained a quote from the research from the University of Mal aga,"However,many existing tools in the domain of open-source digital twins co ncentrate on specific use cases and do not provide a versatile framework. In con trast,the open-source digital twin framework,OpenTwins,aims to provide a vers atile framework that can be applied to a wide range of digital twin applications . In this article,we introduce a re-definition of the original OpenTwins platfo rm that enables the management of custom simulation services and the management of FMI simulation services,which is one of the most widely used simulation stan dards in the industry and its coexistence with machine learning models,which en ables the definition of the next-gen digital twins. Thanks to this integration,digital twins that reflect reality better can be developed,through hybrid model s,where simulation data can feed the scarcity of machine learning data and so f orth. As part of this project,a simulation model developed through the hydrauli c software Epanet was validated in OpenTwins,in addition to an FMI simulation s ervice. The hydraulic model was implemented and tested in an agricultural use ca se in collaboration with the University of Cordoba,Spain."
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    University Magna Graecia of Catanzaro Reports Findings in Artificial Intelligenc e [Artificial intelligence (AI)-assisted chest computer tomog raphy (CT) insights: a study on intensive care unit (ICU) admittance trends in 7 8 coronavirus disease ...]

    88-89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 Artificial Intelligenc e is the subject of a report. According to news originating from Catanzaro,Ital y,by NewsRx correspondents,research stated,"The global coronavirus disease 20 19 (COVID-19) pandemic has posed substantial challenges for healthcare systems,notably the increased demand for chest computed tomography (CT) scans,which lac k automated analysis. Our study addresses this by utilizing artificial intellige nce-supported automated computer analysis to investigate lung involvement distri bution and extent in COVID-19 patients." Our news journalists obtained a quote from the research from the University Magn a Graecia of Catanzaro,"Additionally,we explore the association between lung i nvolvement and intensive care unit (ICU) admission,while also comparing compute r analysis performance with expert radiologists' assessments. A total of 81 pati ents from an open-source COVID database with confirmed COVID-19 infection were i ncluded in the study. Three patients were excluded. Lung involvement was assesse d in 78 patients using CT scans,and the extent of infiltration and collapse was quantified across various lung lobes and regions. The associations between lung involvement and ICU admission were analysed. Additionally,the computer analysi s of COVID-19 involvement was compared against a human rating provided by radiol ogical experts. The results showed a higher degree of infiltration and collapse in the lower lobes compared to the upper lobes (P <0.05). N o significant difference was detected in the COVID-19-related involvement of the left and right lower lobes. The right middle lobe demonstrated lower involvemen t compared to the right lower lobes (P <0.05). When examini ng the regions,significantly more COVID-19 involvement was found when comparing the posterior. the anterior halves and the lower. the upper half of the lungs. who required ICU admission during their treatment exhibited significantly higher COVID-19 involvement in their lung parenchyma according to computer analysis,c ompared to patients who remained in general wards. Patients with more than 40% COVID-19 involvement were almost exclusively treated in intensive care. A high c orrelation was observed between computer detection of COVID-19 affections and th e rating by radiological experts. The findings suggest that the extent of lung i nvolvement,particularly in the lower lobes,dorsal lungs,and lower half of the lungs,may be associated with the need for ICU admission in patients with COVID -19. Computer analysis showed a high correlation with expert rating,highlightin g its potential utility in clinical settings for assessing lung involvement. Thi s information may help guide clinical decision-making and resource allocation du ring ongoing or future pandemics."
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    Study Results from Ain Shams University in the Area of Machine Learning Publishe d (Machine-Learning-Based Traffic Classification in Software-Defined Networks)

    89-90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 originating from Cairo,Egypt,by NewsRx cor respondents,research stated,"Many research efforts have gone into upgrading an tiquated communication network infrastructures with better ones to support conte mporary services and applications. Smart networks can adapt to new technologies and traffic trends on their own." Our news correspondents obtained a quote from the research from Ain Shams Univer sity: "Softwaredefined networking (SDN) separates the control plane from the da ta plane and runs programs in one place,changing network management. New techno logies like SDN and machine learning (ML) could improve network performance and QoS. This paper presents a comprehensive research study on integrating SDN with ML to improve network performance and quality-of-service (QoS). The study primar ily investigates ML classification methods,highlighting their significance in t he context of traffic classification (TC). Additionally,traditional methods are discussed to clarify the ML outperformance observed throughout our investigatio n,underscoring the superiority of ML algorithms in SDN TC. The study describes how labeled traffic data can be used to train ML models for appropriately classi fying SDN TC flows. It examines the pros and downsides of dynamic and adaptive T C using ML algorithms. The research also examines how ML may improve SDN securit y."
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    Hangzhou Dianzi University Reports Findings in Machine Learning (Nonionic surfac tant Tween 80-facilitated bacterial transport in porous media: A nonmonotonic co ncentration-dependent performance,mechanism,and machine learning prediction)

    90-91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 Zhejiang,People's Rep ublic of China,by NewsRx correspondents,research stated,"The surfactant-enhan ced bioremediation (SEBR) of organic-contaminated soil is a promising soil remed iation technology,in which surfactants not only mobilize pollutants,but also a lter the mobility of bacteria. However,the bacterial response and underlying me chanisms remain unclear." Our news journalists obtained a quote from the research from Hangzhou Dianzi Uni versity,"In this study,the effects and mechanisms of action of a selected noni onic surfactant (Tween 80) on Pseudomonas aeruginosa transport in soil and quartz sand were investigated. The results showed that ba cterial migration in both quartz sand and soil was significantly enhanced with i ncreasing Tween 80 concentration,and the greatest migration occurred at a criti cal micelle concentration (CMC) of 4 for quartz sand and 30 for soil,with incre ases of 185.2% and 27.3%,respectively. The experimen tal results and theoretical analysis indicated that Tween 80-facilitated bacteri al migration could be mainly attributed to competition for soil/sand surface sor ption sites between Tween 80 and bacteria. The prior sorption of Tween 80 onto s and/soil could diminish the available sorption sites for P. aeruginosa,resulting in significant decreases in deposition parameters (70.8% and 33.3% decrease in K in sand and soil systems,respectively),t hereby increasing bacterial transport. In the bacterial post-sorption scenario,the subsequent injection of Tween 80 washed out 69.8% of the bacte ria retained in the quartz sand owing to the competition of Tween 80 with pre-so rbed bacteria,as compared with almost no bacteria being eluted by NaCl solution . Several machine learning models have been employed to predict Tween 80-facilia ted bacterial transport. The results showed that back-propagation neural network (BPNN)-based machine learning could predict the transport of P. aeruginosa through quartz sand with Tween 80 in-sample (2 CMC) and out-of-sample (10 CMC) with errors of 0.79% and 3.77%,respectively."
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    Data on Personalized Medicine Reported by Lorenzo Gios and Colleagues [Artificial intelligence of imaging and clinical neurological data for predictive ,preventive and personalized (P3) medicine for Parkinson Disease: The NeuroArtP 3 protocol for ...]

    91-92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 Drugs and Therapies - Personalized Medicine is the subject of a report. According to news reporting or iginating in Trento,Italy,by NewsRx journalists,research stated,"The burden of Parkinson Disease (PD) represents a key public health issue and it is essenti al to develop innovative and cost-effective approaches to promote sustainable di agnostic and therapeutic interventions. In this perspective the adoption of a P3 (predictive,preventive and personalized) medicine approach seems to be pivotal ." Financial support for this research came from Ministero della Salute. The news reporters obtained a quote from the research,"The NeuroArtP3 (NET-2018 -12366666) is a four-year multi-site project co-funded by the Italian Ministry o f Health,bringing together clinical and computational centers operating in the field of neurology,including PD. The core objectives of the project are: i) to harmonize the collection of data across the participating centers,ii) to struct ure standardized disease-specific datasets and iii) to advance knowledge on dise ase's trajectories through machine learning analysis. The 4-years study combines two consecutive research components: i) a multi-center retrospective observatio nal phase; ii) a multi-center prospective observational phase. The retrospective phase aims at collecting data of the patients admitted at the participating cli nical centers. Whereas the prospective phase aims at collecting the same variabl es of the retrospective study in newly diagnosed patients who will be enrolled a t the same centers. The participating clinical centers are the Provincial Health Services (APSS) of Trento (Italy) as the center responsible for the PD study an d the IRCCS San Martino Hospital of Genoa (Italy) as the promoter center of the NeuroartP3 project. The computational centers responsible for data analysis are the Bruno Kessler Foundation of Trento (Italy) with TrentinoSalute4.0 -Competenc e Center for Digital Health of the Province of Trento (Italy) and the LISCOMPlab University of Genoa (Italy)."
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    Research from University of Siena Yields New Study Findings on Robotics [One small step for a robot,one giant leap for habitat monitoring: A structural survey of EU forest habitats with Roboticallymounted Mobile Laser Scanning (RML S)]

    92-93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 from Siena,Italy,by NewsRx journa lists,research stated,"EU States are mandated by the 92/43/EEC Habitats Direct ive to generate recurring reports on the conservation status and functionality o f habitats at the national level. This assessment is based on their floristic an d,especially for forest habitats,structural characterization." Our news reporters obtained a quote from the research from University of Siena: "Currently,habitat field monitoring efforts are carried out only by trained hum an operators. The H2020 Project ‘Natural Intelligence for Robotic Monitoring of Habitats - NI' aims to develop quadrupedal robots able to move autonomously in t he unstructured environment of forest habitats. In this work,we tested the loco motion performance,efficiency and the accuracy of a robot performing structural habitat monitoring,comparing it with traditional field survey methods inside s elected stands of Luzulo-Fagetum beech forests (9110 Annex I Habitat). We used a quadrupedal robot equipped with a Mobile Laser Scanning system (MLS),implement ing for the first time a structural monitoring of EU forest habitats with a Robo tically-mounted Mobile Laser Scanning (RMLS) platform. Two different scanning tr ajectories were used to automatically map individual tree locations and extract tree Diameter at Breast Height (DBH) from point clouds. Results were compared wi th field human measurements in terms of accuracy and efficiency of the survey. T he robot was able to successfully execute both scanning trajectories,for which we obtained a tree detection rate of 100 %. Circular scanning traje ctory performed better in terms of battery consumption,Root Mean Square Error ( RMSE) of the extracted DBH (2.43 cm or 10.73 %) and prediction powe r (R2adj = 0.72,p <0.001). The RMLS platform improved sur vey efficiency with 19.31 m2/min or 1.77 trees/min in comparison with 3.45 m2/mi n or 0.32 trees/min of traditional survey."
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    Reports from Harbin Institute of Technology Provide New Insights into Robotics ( Interactive Coupling of Structural Dynamics and Milling Forces for High-frequenc y Stability Prediction In Robotic Milling)

    93-94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 in Harbin,People's Republic of Chi na,by NewsRx journalists,research stated,"Robotic machining has developed rap idly and has great potential for manufacturing large parts with complex surfaces . However,the machining quality and efficiency are severely limited by the low stiffness of the robots,which causes destructive chatter during manufacturing." Funders for this research include National Natural Science Foundation of China ( NSFC),Guangdong Basic and Applied Basic Research Foundation,Science and Techno logy Innovation Committee of Shenzhen,Shenzhen Peacock Innovation Team Project. The news reporters obtained a quote from the research from the Harbin Institute of Technology,"To suppress this phenomenon,offline stability prediction must b e performed before the actual robotic milling. In this study,a high-frequency s tability prediction algorithm for robotic milling was developed by introducing a n interactive method. A lightweight structural dynamic model of a robot was deve loped based on specially designed decoupling criteria,and an interpolation algo rithm that determines the trajectory of the tool nose was designed to calculate the milling force. By combining the two models,an interactive coupling algorith m was proposed to forecast the vibration in robotic milling. The predicted resul ts can contribute to generating the stability lobe,thereby verifying the stabil ity distribution and spectral characteristics of both the simulated and measured signals."
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    University of California Reports Findings in Artificial Intelligence (Search Eng ines and Generative Artificial Intelligence Integration: Public Health Risks and Recommendations to Safeguard Consumers Online)

    94-95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 Artificial Intelligenc e is the subject of a report. According to news originating from La Jolla,Calif ornia,by NewsRx correspondents,research stated,"The online pharmacy market is growing,with legitimate online pharmacies offering advantages such as convenie nce and accessibility. However,this increased demand has attracted malicious ac tors into this space,leading to the proliferation of illegal vendors that use d eceptive techniques to rank higher in search results and pose serious public hea lth risks by dispensing substandard or falsified medicines." Our news journalists obtained a quote from the research from the University of C alifornia,"Search engine providers have started integrating generative artifici al intelligence (AI) into search engine interfaces,which could revolutionize se arch by delivering more personalized results through a user-friendly experience. However,improper integration of these new technologies carries potential risks and could further exacerbate the risks posed by illicit online pharmacies by in advertently directing users to illegal vendors. The role of generative AI integr ation in reshaping search engine results,particularly related to online pharmac ies,has not yet been studied. Our objective was to identify,determine the prev alence of,and characterize illegal online pharmacy recommendations within the A I-generated search results and recommendations. We conducted a comparative asses sment of AI-generated recommendations from Google's Search Generative Experience (SGE) and Microsoft Bing's Chat,focusing on popular and well-known medicines r epresenting multiple therapeutic categories including controlled substances. Web sites were individually examined to determine legitimacy,and known illegal vend ors were identified by cross-referencing with the National Association of Boards of Pharmacy and LegitScript databases. Of the 262 websites recommended in the A I-generated search results,47.33% (124/262) belonged to active on line pharmacies,with 31.29% (82/262) leading to legitimate ones. However,19.04% (24/126) of Bing Chat's and 13.23% ( 18/136) of Google SGE's recommendations directed users to illegal vendors,inclu ding for controlled substances. The proportion of illegal pharmacies varied by d rug and search engine. A significant difference was observed in the distribution of illegal websites between search engines. The prevalence of links leading to illegal online pharmacies selling prescription medications was significantly hig her (P=.001) in Bing Chat (21/86,24%) compared to Google SGE (6/92 ,6%). Regarding the suggestions for controlled substances,suggest ions generated by Google led to a significantly higher number of rogue sellers ( 12/44,27%; P=.02) compared to Bing (3/40,7%). While the integration of generative AI into search engines offers promising potential,it also poses significant risks. This is the first study to shed light on the v ulnerabilities within these platforms while highlighting the potential public he alth implications associated with their inadvertent promotion of illegal pharmac ies. We found a concerning proportion of AI-generated recommendations that led t o illegal online pharmacies,which could not only potentially increase their tra ffic but also further exacerbate existing public health risks."
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    Cranfield University Reports Findings in Machine Learning (High-Throughput Scree ning of Sulfur-Resistant Catalysts for Steam Methane Reforming Using Machine Lea rning and Microkinetic Modeling)

    95-96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 Bedfordshire,United Kin gdom,by NewsRx journalists,research stated,"The catalytic activity of bimetal lic catalysts for the steam methane reforming (SMR) reaction was extensively stu died previously. However,the performance of these materials in the presence of sulfur-containing species is yet to be investigated." The news correspondents obtained a quote from the research from Cranfield Univer sity,"In this study,we propose a novel process aided by machine learning (ML) and microkinetic modeling for the rapid screening of sulfur-resistant bimetallic catalysts. First,various ML models were developed to predict atomic adsorption energies (C,H,O,and S) on bimetallic surfaces. Easily accessible physical an d chemical properties of the metals and adsorbates were used as input features. The Ensemble learning,artificial neural network,and support vector regression models achieved the best performance with values of 0.74,0.71,and 0.70,respec tively. A microkinetic model was then built based on the elementary steps of the SMR reaction. Finally,the microkinetic model,together with the atomic adsorpt ion energies predicted by the Ensemble model,were used to screen over 500 bimet allic materials."
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