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    Studies from Ningbo University Further Understanding of Artificial Intelligence (The Innovative Construction of Provinces, Regional Artificial Intelligence Deve lopment, and the Resilience of Regional Innovation Ecosystems: Quasi-Natural ... )

    38-38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 new report. According to news reporting out of Ningbo, Peo ple’s Republic of China, by NewsRx editors, research stated, “Based on the theor y of regional innovation niches, this study calculates the resilience of regiona l innovation ecosystems and constructs a comprehensive evaluation index system f or regional artificial intelligence development, resulting in a panel dataset fo r 30 provinces in China from 2009 to 2021 (excluding Tibet, Hong Kong, Macao, an d Taiwan).” Funders for this research include National Social Science Foundation of China; R esearch Project of Ningbo Urban Civilization Research Institute.
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    University of Sydney Reports Findings in Artificial Intelligence (Attitudes and Perceptions of Australian Dentists and Dental Students Towards Applications of A rtificial Intelligence in Dentistry: A Survey)

    39-39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 reporting originating in Sydney, Australia, by NewsRx journalists, research stated, “As artificial intelligence (AI) rapidly evolves in dentistry, understanding dentists’ and dental students’ perspectives is key. This survey evaluated Australian dentists’ and students’ at titudes and perceptions of AI in dentistry.” The news reporters obtained a quote from the research from the University of Syd ney, “An online questionnaire developed on Qualtrics was distributed among regis tered Australian dentists and students enrolled in accredited Australian dental or oral health programmes. Descriptive and bivariate analyses were used to exami ne the demographic variables and participant attitudes. 177 responses were recei ved, and 155 complete responses were used in data analysis. 54.8% were aware of dental AI applications, but 70.3 % could not name a s pecific AI software. A majority (91.6%) viewed AI as a supportive t ool, with 69 % believing that it would be beneficial in clinical ta sks and 35.6% expecting it to perform similarly to an average spec ialist. 40% anticipated that dental AI would be routinely used in the next 5-10 years, with more dental students expecting this short-term integra tion. Concerns included job displacement, inflexibility in patient care, and mis trust of AI’s accuracy. Attitudes towards AI were influenced by age, gender, cli nical experience and technological proficiency. The survey underscores the poten tial of AI to revolutionise dental care, enhancing clinical workflows and decisi on-making. However, challenges like trust in AI and ethical concerns remain. It is recommended that practising dentists receive hands-on training with AI tools and continuing dental education programmes.”
      原文链接:
    • NETL
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    Data on Machine Learning Discussed by Researchers at Shanghai Jiao Tong Universi ty (Decoding Missense Variants by Incorporating Phase Separation via Machine Lea rning)

    40-40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 artificial in telligence have been published. According to news reporting from Shanghai Jiao T ong University by NewsRx journalists, research stated, “Computational models hav e made significant progress in predicting the effect of protein variants.” The news correspondents obtained a quote from the research from Shanghai Jiao To ng University: “However, deciphering numerous variants of uncertain significance (VUS) located within intrinsically disordered regions (IDRs) remains challengin g. To address this issue, we introduce phase separation, which is tightly linked to IDRs, into the investigation of missense variants. Phase separation is vital for multiple physiological processes. By leveraging missense variants that alte r phase separation propensity, we develop a machine learning approach named PSMu tPred to predict the impact of missense mutations on phase separation. PSMutPred demonstrates robust performance in predicting missense variants that affect nat ural phase separation. In vitro experiments further underscore its validity.”
      原文链接:
    • NETL
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    University of Michigan Reports Findings in Machine Learning (Machine Learning Pr edictions of Methane Storage in MOFs: Diverse Materials, Multiple Operating Cond itions, and Reverse Models)

    40-41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 from Ann Arbor, M ichigan, by NewsRx correspondents, research stated, “A machine learning (ML) mod el is developed for predicting useable methane (CH) capacities in metal-organic frameworks (MOFs). The model applies to a wide variety of MOFs, including those with and without open metal sites, and predicts capacities for multiple pressure swing conditions.”
      原文链接:
    • NETL
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    Research Reports from Xinjiang Agricultural University Provide New Insights into Machine Learning (Identification and Monitoring of Irrigated Areas in Arid Area s Based on Sentinel-2 Time-Series Data and a Machine Learning Algorithm)

    41-42页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Investigators publish new report on artificial in telligence. According to news reporting from Urumqi, People’s Republic of China, by NewsRx journalists, research stated, “Accurate monitoring of irrigation area s is of great significance to ensure national food security and rational utiliza tion of water resources. The low resolution of the Moderate Resolution Imaging S pectroradiometer and Landsat data makes the monitoring accuracy insufficient for actual demand.” Financial supporters for this research include Xinjiang Uygur Autonomous Region Major Project; National Natural Science Foundation of China; Xinjiang Key Labora tory of Water Conservancy Engineering Safety And Water Disaster Prevention Open Project; Top-level Project of The Belt And Road Water And Sustainable Developmen t Science And Technology Fund of The National Key Laboratory of Water Disaster D efense; Xin-jiang Uygur Autonomous Region People’s Government.
      原文链接:
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    Findings from Linkoping University Yields New Findings on Machine Learning (Deep Svbrdf Acquisition and Modelling: a Survey)

    42-43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 from Norrkoping, Sweden, by NewsRx journalists, research stated, “Hand in hand with the rapid development o f machine learning, deep learning and generative AI algorithms and architectures , the graphics community has seen a remarkable evolution of novel techniques for material and appearance capture. Typically, these machine-learning-driven metho ds and technologies, in contrast to traditional techniques, rely on only a singl e or very few input images, while enabling the recovery of detailed, high-qualit y measurements of bi-directional reflectance distribution functions, as well as the corresponding spatially varying material properties, also known as Spatially Varying Bi-directional Reflectance Distribution Functions (SVBRDFs).” Financial support for this research came from European Union (EU).
      原文链接:
    • NETL
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    Instituto Tecnologico Metropolitano Researcher Describes Recent Advances in Mach ine Learning (Characterization of Maize, Common Bean, and Avocado Crops under Ab iotic Stress Factors Using Spectral Signatures on the Visible to Near-Infrared . ..)

    43-44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 originating from Medellin, Colombia, by New sRx correspondents, research stated, “Abiotic stress factors can be detected usi ng visible and near-infrared spectral signatures.” Funders for this research include Ministerio De Ciencia, Tecnologia E Innovacion -minciencias, Colombia.
      原文链接:
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    Study Findings from School of Electrical Engineering Advance Knowledge in Roboti cs (Contact Force Control of Robot Polishing System Based on Vision Control Algo rithm)

    44-45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 robotics. A ccording to news reporting from Changzhou, People’s Republic of China, by NewsRx journalists, research stated, “In this study, an autonomous robotic polishing s ystem leveraging sensor signal processing and control technology is developed. I t utilizes a primary-secondary configuration with machine vision and force senso rs for intelligent defect detection.” Financial supporters for this research include Qinglan Project of Jiangsu Provin ce of China.
      原文链接:
    • NETL
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    Data on Attention Deficit Hyperactivity Disorders Reported by Mahi Khemchandani and Colleagues [Comparative analysis of electroencephalogram (EEG) data gathered from the frontal region with other brain regions affected by attention deficit ...]

    45-46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 Developmental Diseases and Conditions - Attention Deficit Hyperactivity Disorders is the subject of a report. According to news reporting from Mumbai, India, by NewsRx journalists, r esearch stated, “Attention deficit hyperactivity disorder (ADHD) is a neurodevel opmental disorder characterized by repeated patterns of hyperactivity, impulsivi ty, and inattention that limit daily functioning and development. Electroencepha lography (EEG) anomalies correspond to changes in brain connection and activity. ” The news correspondents obtained a quote from the research, “The authors propose utilizing empirical mode decomposition (EMD) and discrete wavelet transform (DW T) for feature extraction and machine learning (ML) algorithms to categorize ADH D and control subjects. For this study, the authors considered freely accessible ADHD data obtained from the IEEE data site. Studies have demonstrated a range o f EEG anomalies in ADHD patients, such as variations in power spectra, coherence patterns, and event-related potentials (ERPs). Some of the studies claimed that the brain’s prefrontal cortex and frontal regions collaborate in intricate netw orks, and disorders in either of them exacerbate the symptoms of ADHD. , Based o n the research that claimed the brain’s prefrontal cortex and frontal regions co llaborate in intricate networks, and disorders in either of them exacerbate the symptoms of ADHD, the proposed study examines the optimal position of EEG electr ode for identifying ADHD and in addition to monitoring accuracy on frontal/ pref rontal and other regions of brain our study also investigates the position group ings that have the highest effect on accurateness in identification of ADHD. The results demonstrate that the dataset classified with AdaBoost provided values f or accuracy, precision, specificity, sensitivity, and F1-score as 1.00, 0.70, 0. 70, 0.75, and 0.71, respectively, whereas using random forest (RF) it is 0.98, 0 .64, 0.60, 0.81, and 0.71, respectively, in detecting ADHD. After detailed analy sis, it is observed that the most accurate results included all electrodes.”
      原文链接:
    • NETL
    • NSTL

    University of Sydney Reports Findings in Machine Learning (Comparison of a machi ne learning model with a conventional rule-based selective dry cow therapy algor ithm for detection of intramammary infections)

    46-47页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – New research on Machine Learning is the subject o f a report. According to news originating from Camden, Australia, by NewsRx corr espondents, research stated, “We trained machine learning models to identify int ramammary infections (IMI) in late lactation cows at dry-off to guide antibiotic treatment, and compared their performance to a rule-based algorithm that is cur rently used on dairy farms in the US. We conducted an observational test-charact eristics study using a data set of 3,645 cows approaching dry-off from 68 US dai ry herds.” Our news journalists obtained a quote from the research from the University of S ydney, “The outcome variables of interest were cow-level IMI caused by all patho gens, major pathogens, and Streptococcus and Strep-like organisms (SSLO), which were determined using aerobic culture of aseptic quarter-milk samples and identi fication of isolates using MALDI-TOF. Individual cow records were extracted from the farm software to create 53 feature variables at the cow and 39 at the herd- level which were derived from cowlevel descriptive data, records of clinical ma stitis events, results from routine testing of milk for volume and concentration s of somatic cell count (SCC), fat, and protein. ML algorithms evaluated were lo gistic regression, decision tree, random forest, light gradient-boosting machine , naive bayes, and neural networks. For comparison, cows were also classified ac cording to a conventional rule-based algorithm that considered a cow as high ris k for IMI if she had at one or more high SCC (>200,000 c ells/ml) tests or 2 cases of clinical mastitis during the lactation of enrollmen t. Area under the curve (AUC) and Youden’s index were used to compare models, in addition to binary classification metrics, including sensitivity, specificity, and predictive values. ML models had slightly higher AUC and Youden’s index valu es than the rule-based algorithm for all IMI outcomes of interest. However, thes e improvements in prediction accuracy were substantially less than what we had c onsidered necessary for the technology to be a worthwhile alternative to the rul e-based algorithm. Therefore, evidence is lacking to support the wholesale use o f ML-guided selective dry cow therapy at the moment.”
      原文链接:
    • NETL
    • NSTL