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    Researchers from Wuhan University Report Findings in Artificial Intelligence (Fr om Hearing To Seeing: Linking Auditory and Visual Place Perceptions With Soundsc ape-to-image Generative Artificial Intelligence)

    29-30页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Data detailed on Artificial Intelligen ce have been presented. According to news reporting originating from Wuhan, Peop le's Republic of China, by NewsRx correspondents, research stated, "People exper ience the world through multiple senses simultaneously, contributing to our sens e of place. Prior quantitative geography studies have mostly emphasized human vi sual perceptions, neglecting human auditory perceptions at place due to the chal lenges in characterizing the acoustic environment vividly." Funders for this research include National Natural Science Foundation of China ( NSFC), Wuhan University, Key Laboratory of Resources and Environmental Informati on System.
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    New Machine Learning Findings Reported from Aarhus University [Cascading Symmetry Constraint During Machine Learning-enabled Structural Search for Sulfur-induced Cu(111)-( 43 X 43) Surface Reconstruction]

    30-31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 Aarhus, Denm ark, by NewsRx correspondents, research stated, "In this work, we investigate ho w exploiting symmetry when creating and modifying structural models may speed up global atomistic structure optimization." Financial supporters for this research include Danmarks Grundforskningsfond, Vil lum Fonden through Investigator Grant, Danish National Research Foundation throu gh the Center of Excellence "InterCat."Our news editors obtained a quote from the research from Aarhus University, "We propose a search strategy in which models start from high symmetry configuration s and then gradually evolve into lower symmetry models. The algorithm is named c ascading symmetry search and is shown to be highly efficient for a number of kno wn surface reconstructions."
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    New Findings from Chongqing Jianzhu College in the Area of Machine Learning Desc ribed (A Fusion of Neural, Genetic and Ensemble Machine Learning Approaches for Enhancing the Engineering Predictive Capabilities of Lightweight Foamed Reinforc ed ...)

    31-32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 Chongqing, People's Republic of Ch ina, by NewsRx editors, research stated, "This research explores lightweight foa med reinforced concrete beams, crucial in modern construction for their strength and reduced weight. It introduces a novel approach, integrating three machine l earning models: Neural Networks (NNs), Genetic Algorithms (GAs), and Ensemble Te chniques, especially Gradient Boosting Machines (GBM)." Funders for this research include Science and Technology Research Program of Cho ngqing Municipal Education Commission, Chongqing Construction Science and Techno logy Programme Project (2023), Princess Nourah bint Abdulrahman University Resea rchers Supporting Project, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia, Prince Sattam bin Abdulaziz University.
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    Study Data from Federal University of Santa Catarina Update Knowledge of Machine Learning (Reliability and Validity of an Automated Model for Assessing the Lear ning of Machine Learning in Middle and High School: Experiences from the 'ML for ...)

    32-33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 ar tificial intelligence. According to news reporting originating from Federal Univ ersity of Santa Catarina by NewsRx correspondents, research stated, "The inserti on of Machine Learning (ML) in everyday life demonstrates the importance of popu larizing an understanding of ML already in school." Our news journalists obtained a quote from the research from Federal University of Santa Catarina: "Accompanying this trend arises the need to assess the studen ts' learning. Yet, so far, few assessments have been proposed, most lacking an e valuation. Therefore, we evaluate the reliability and validity of an automated a ssessment of the students' learning of an image classification model created as a learning outcome of the ‘ML for All!' course. Results based on data collected from 240 students indicate that the assessment can be considered reliable (coeff icient Omega = 0.834/Cronbach's alpha a=0.83). We also identified moderate to st rong convergent and discriminant validity based on the polychoric correlation ma trix. Factor analyses indicate two underlying factors ‘Data Management and Model Training' and ‘Performance Interpretation', completing each other."
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    Studies from Xi'an Jiaotong University Reveal New Findings on Machine Learning ( Exploring the Influence of Crystallization Fouling On Microscale Heat Exchangers Through Machine Learning Analysis)

    33-34页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Research findings on Machine Learning are discussed in a new report. According to news reporting from Xi'an, People's Republic of China, by NewsRx editors, the research stated, "Crystallization depo sition within micro-scale heat exchangers poses significant challenges to their efficiency and functionality, stemming from the accumulation of crystalline resi dues on heat transfer surfaces. This study employs advanced machine learning met hodologies, including GRU, LSTM, RNN, and CNN models, to explore the underlying factors influencing micro heat exchanger performance." The news correspondents obtained a quote from the research from Xi'an Jiaotong U niversity, "Through meticulous analysis of key parameters such as Reynolds numbe r, sedimentation coefficient, flow rate, and channel dimensions, the study aims to delineate the foundational factors shaping heat exchanger performance at the microscopic level. Results reveal the exceptional accuracy of CNN model in forec asting experimental outcomes, surpassing 99% accuracy and demonstr ating superior performance compared to traditional numerical methods. Temperatur e emerges as a pivotal determinant, profoundly influencing crystallization dynam ics, with its intricate interplay with solute solubility elucidated through rigo rous analysis. Furthermore, comparative assessment of training times highlights the CNN model's efficiency, attributed to its specialized architecture suited fo r spatial data processing."
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    National and Kapodistrian University of Athens Reports Findings in Occupational Dermatitis (Preservative contact allergy in occupational dermatitis: a machine l earning analysis)

    34-35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 Occupational Diseases and Conditions - Occupational Dermatitis is the subject of a report. According t o news reporting originating in Athens, Greece, by NewsRx journalists, research stated, "Occupational dermatoses impose a significant socioeconomic burden. Alle rgic contact dermatitis related to occupation is prevalent among healthcare work ers, cleaning service personnel, individuals in the beauty industry and industri al workers." The news reporters obtained a quote from the research from the National and Kapo distrian University of Athens, "Among risk factors, the exposure to preservative s is frequent, since they are extensively added in products for occupational use . The goal of this study is to investigate the contact allergy patterns in order to understand the linkage among hypersensitivity to preservatives, occupational profiles, patients' clinical and demographic characteristics. Patch test result s were collected from monosensitized patients to Formaldehyde 2%, K ATHON 0.02%, thimerosal 0.1%, and MDBGN 0.5% ; information was also collected for an extended MOAHLFA (Male-Occupational-Atop ic-Hand-Leg-Face-Age) index. To assess the relationship between allergen group a nd occupational-related ACD, the chi-square test for independence was utilized. To uncover underlying relationships in the data, multiple correspondence analysi s (MCA) and categorical principal components analysis (CATPCA), which are machin e learning approaches, were applied. Significant relationships were found betwee n allergen group and: occupation class, atopy, hand, leg, facial, trunk, neck, h ead dermatitis, clinical characteristics, ICDRG 48 h and ICDRG 72 h clinical eva luation. MCA and CATPCA findings revealed a link among allergen group, occupatio n class, patients' demographic and clinical characteristics, the MOAHLFA index, and the ICDRG scores. Significant relationships were identified between the alle rgen group and various manifestations of dermatitis."
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    Zhongnan Hospital of Wuhan University Reports Findings in Cancer Biomarkers (CBi oProfiler: A Web and Standalone Pipeline for Cancer Biomarker and Subtype Charac terization)

    35-36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 Diagnostics and Screen ing - Cancer Biomarkers is the subject of a report. According to news reporting originating from Wuhan, People's Republic of China, by NewsRx correspondents, re search stated, "Cancer is a leading cause of death worldwide, and the identifica tion of biomarkers and subtypes that can predict the long-term survival of cance r patients is essential for their risk stratification, treatment, and prognosis. However, there are currently no standardized tools for exploring cancer biomark ers or subtypes." Our news editors obtained a quote from the research from the Zhongnan Hospital o f Wuhan University, "In this study, we introduced Cancer Biomarker and Subtype P rofiler (CBioProfiler), a web server and standalone application that includes tw o pipelines for analyzing cancer biomarkers and subtypes. The cancer biomarker p ipeline consists of five modules for identifying and annotating cancer survival- related biomarkers using multiple survival-related machine learning algorithms. The cancer subtype pipeline includes three modules for data preprocessing, subty pe identification using multiple unsupervised machine learning methods, as well as subtype evaluation and validation. CBioProfiler also includes CuratedCancerPr ognosisData, a novel R package that integrates reviewed and curated gene express ion and clinical data from 268 studies. These studies cover 43 common blood and solid tumors and draw upon 47,686 clinical samples."
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    Findings from Kansas State University in the Area of Machine Learning Reported ( Treed Gaussian Processes for Animal Movement Modeling)

    36-37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 Manhattan, Kansas, by NewsRx correspondents, research stated, "Wildlife telemetry data may be used to answer a diverse range of questions relevant to wildlife ecology and manageme nt. One challenge to modeling telemetry data is that animal movement often varie s greatly in pattern over time, and current continuous-time modeling approaches to handle such nonstationarity require bespoke and often complex models that may pose barriers to practitioner implementation." Financial supporters for this research include Kansas State University, Kansas S tate University's Lolafaye Coyne research scholarship, U.S. Geological Survey th rough the Kansas Cooperative Fish and Wildlife Research Unit through Research Wo rk Order 70.
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    Mohammed V University in Rabat Reports Findings in Breast Cancer (Predicting dis ease recurrence in breast cancer patients using machine learning models with cli nical and radiomic characteristics: a retrospective study)

    37-38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 Oncology - Breast Canc er is the subject of a report. According to news reporting from Rabat, Morocco, by NewsRx journalists, research stated, "The goal is to use three different mach ine learning models to predict the recurrence of breast cancer across a very het erogeneous sample of patients with varying disease kinds and stages. A heterogen eous group of patients with varying cancer kinds and stages, including both trip le-negative breast cancer (TNBC) and non-triple-negative breast cancer (non-TNBC ), was examined." The news correspondents obtained a quote from the research from Mohammed V Unive rsity in Rabat, "Three distinct models were created using the following five mac hine learning techniques: Adaptive Boosting (AdaBoost), Random Under-sampling Bo osting (RUSBoost), Extreme Gradient Boosting (XGBoost), support vector machines (SVM), and Logistic Regression. The clinical model used both clinical and pathol ogy data in conjunction with the machine learning algorithms. The machine learni ng algorithms were combined with dynamic contrast-enhanced magnetic resonance im aging (DCE-MRI) imaging characteristics in the radiomic model, and the merged mo del combined the two types of data. Each technique was evaluated using several c riteria, including the receiver operating characteristic (ROC) curve, precision, recall, and F1 score. The results suggest that the integration of clinical and radiomic data improves the predictive accuracy in identifying instances of breas t cancer recurrence. The XGBoost algorithm is widely recognized as the most effe ctive algorithm in terms of performance. The findings presented in this study of fer significant contributions to the field of breast cancer research, particular ly in relation to the prediction of cancer recurrence."
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    Studies from Sahand University of Technology Yield New Data on Machine Learning (Low salinity water flooding: estimating relative permeability and capillary pre ssure using coupling of particle swarm optimization and machine learning techniq ue)

    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 originating from S ahand University of Technology by NewsRx correspondents, research stated, "The r eservoir's properties are required for proper reservoir simulation, which also i nvolves uncertainties. Experimental methods to estimate the relative permeabilit y and capillary pressure data are expensive and time-consuming." Our news reporters obtained a quote from the research from Sahand University of Technology: "This study aims to determine the relative permeability and capillar y pressure functions of a sandstone core in the presence and absence of clay dur ing low-salinity water floods. The data were provided by automatic history match ing the results from previously lab-reported studies through coupling a simulato r with the particle swarm optimization algorithm. Correlations were proposed usi ng multiple-linear regression for relative permeability and capillary pressure p arameters at low-salinity conditions. They were validated against experimental r esults of no clay and clayey formation with regression of 95% and 97%. To assign one curve of relative permeability and capillary pre ssure to the grid cells of the simulator, averaging techniques were implemented. The effect of salinity and clay content on the obtained curves was investigated . Changing salinity from 42000 to 4000 ppm, the reduction in water relative perm eability appeared to be higher than the oil relative permeability increment."
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