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    Reports Summarize Artificial Intelligence Study Results from University of Techn ology and Applied Sciences (To What Extent Has Artificial Intelligence Impacted EFL Teaching and Learning? A Systematic Review)

    77-78页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News-New research on artificial intelligenc e is the subject of a new report. According tonews reporting out of the Univers ity of Technology and Applied Sciences by NewsRx editors, researchstated, "Util izing artificial intelligence (AI) technologies in EFL teaching and learning has brought about unimaginable opportunities to enhance learners' fluency and profi ciency in the target language as it isevident that employing AI tools helps lea rners develop their language skills, enhance engagement andmotivation, ease for eign language anxiety, and ultimately acquire the target language."
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    Studies from University of Bologna Provide New Data on Machine Learning (Unify: a Unified Policy Designing Framework for Solving Integrated Constrained Optimiza tion and Machine Learning Problems)

    78-79页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News-Researchers detail new data in Machine Learning. According to news originatingfrom Bologna, Italy, by NewsRx correspo ndents, research stated, "The integration of Machine Learning(ML) and Constrain ed Optimization (CO) techniques has recently gained significant interest. While pureCO methods struggle with scalability and robustness, and ML methods like co nstrained ReinforcementLearning (RL) face difficulties with combinatorial decis ion spaces and hard constraints, a hybrid approachshows promise."Funders for this research include European ICT-48-2020 Project TAILOR, Horizon E urope projectTUPLES, PNRR, European Commission under the NextGeneration EU prog ram.
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    New Robotics Study Findings Recently Were Reported by Researchers at Tsinghua Un iversity (Influence of Robot Anthropomorphism On Consumer Attitudes Toward Resta urants and Service Providers)

    79-80页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News-A new study on Robotics is now availab le. According to news reporting originatingin Beijing, People's Republic of Chi na, by NewsRx journalists, research stated, "We conducted this studyto investig ate the influence of robot anthropomorphism on consumers' attitudes toward resta urants andservice providers, focusing on the moderating effects of robot social roles and consumer autonomy. Theresults of Study 1 revealed that anthropomorph ic robots fostered more positive attitudes toward restaurantsand service robots than non-anthropomorphic robots when consumers viewed restaurant advertisements ."Financial support for this research came from National Natural Science Foundatio n of China (NSFC).
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    Data on Artificial Intelligence Reported by Etienne Plesiat and Colleagues (Arti ficial intelligence reveals past climate extremes by reconstructing historical r ecords)

    80-81页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News-New research on Artificial Intelligenc e is the subject of a report. According tonews reporting from Hamburg, Germany, by NewsRx journalists, research stated, "The understanding ofrecent climate ex tremes and the characterization of climate risk require examining these extremes withina historical context. However, the existing datasets of observed extreme s generally exhibit spatial gapsand inaccuracies due to inadequate spatial extr apolation."Financial support for this research came from EC | Horizon 2020 Framework Progra mme.The news correspondents obtained a quote from the research, "This problem arises from traditionalstatistical methods used to account for the lack of measuremen ts, particularly prevalent before the mid-20thcentury. In this work, we use art ificial intelligence to reconstruct observations of European climate extremes(w arm and cold days and nights) by leveraging Earth system model data from CMIP6 t hrough transferlearning. Our method surpasses conventional statistical techniqu es and diffusion models, showcasingits ability to reconstruct past extreme even ts and reveal spatial trends across an extensive time span(1901-2018) that is n ot covered by most reanalysis datasets."
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    Studies from International Institute of Information Technology Have Provided New Data on Machine Learning (Spatial Analysis of Land Use Land Cover Dynamics in t he Madurai District Using Sentinel-2Data and Supervised Learning Algorithms)

    81-82页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews-Research findings on artificial intelligence are discussed in a new report. According to newsoriginating from Karnataka, India, by NewsRx correspondents, research stated, "LULC, or Land Use andLand Cover, re fers to the classification and description of different types of land and its us age patterns,including urban areas, forests, agricultural land, etc."Our news journalists obtained a quote from the research from International Insti tute of InformationTechnology: "In remote sensing, satellite imagery for LULC m apping is becoming more widespread.Numerous studies examine various approaches to improve mapping efficiency and accuracy, highlightingthe significance of var ious data sources, machine learning algorithms, and categorization techniques. T hisstudy employs machine learning classifiers, namely Random Forest (RF), Suppo rt Vector Machine (SVM),Gradient Boosted Trees (GTB), Classification and Regres sion Trees (CART), and K-Nearest Neighbors(KNN) for land use and land cover (LU LC) classification of Madurai district utilizing Google Earth Engine.The findin gs reveal the impressive performance of Random Forest, boasting an overall accur acy of 99.01percent coupled with a commendable Kappa coefficient of 98.68. Conv ersely. However, amidst thesecommendable achievements, it's noteworthy to highl ight the nuanced variations observed between theaccuracy of training and valida tion sets."
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    Research Results from University of Munster Update Knowledge of Machine Learning (High-resolution soil temperature and soil moisture patterns in space, depth an d time: An interpretable machine learning modelling approach)

    82-83页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News-New study results on artificial intell igence have been published. According to newsreporting originating from Munster , Germany, by NewsRx correspondents, research stated, "Soil temperatureand soil moisture are key drivers of various soil ecological processes, which implies a significantimportance of datasets including their variations in space, depth an d time (4D)."Funders for this research include German Research Foundation.The news reporters obtained a quote from the research from University of Munster : "Current griddedproducts typically have a low resolution, either spatially or temporally. Here, we aim at modelling andexplaining high-resolution soil tempe rature and soil moisture patterns in 4D for a 400 km2 study area in aheterogene ous landscape. Our target resolution of 10 m in space, 10 cm in depth, and 1 h i n time allowscapturing small-scale variations as well as short-term dynamics. W e used multi-depth soil temperature andsoil moisture measurements from 212 loca tions and linked them to 45 potential predictors, representingmeteorological co nditions, soil parameters, vegetation coverage, and landscape relief. We trained randomforest models that were able to predict soil temperature with a mean abs olute error of 0.93 °C and soilmoisture with a mean absolute error of 4.64 % volumetric water content. Continuous model predictionsenabled a comprehensive a nalysis of 4D patterns and confirmed that the selected resolution is essential to capture soil temperature and soil moisture variations at the landscape scale."
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    University of Washington Reports Findings in Artificial Intelligence (Artificial intelligence-generated feedback on social signals in patient-provider communica tion: technical performance, feedback usability, and impact)

    83-84页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News-New research on Artificial Intelligenc e is the subject of a report. According to newsreporting originating in Seattle , Washington, by NewsRx journalists, research stated, "Implicit bias perpetuateshealth care inequities and manifests in patient-provider interactions, particul arly nonverbal socialcues like dominance. We investigated the use of artificial intelligence (AI) for automated communicationassessment and feedback during pr imary care visits to raise clinician awareness of bias in patient interactions.(1) Assessed the technical performance of our AI models by building a machine-le arning pipelinethat automatically detects social signals in patient-provider in teractions from 145 primary care visits. (2)Engaged 24 clinicians to design usa ble AI-generated communication feedback for their workflow. (3)Evaluated the im pact of our AI-based approach in a prospective cohort of 108 primary care visits ."Funders for this research include National Library of Medicine at the National I nstitutes of Health,National Institutes of Health.
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    Reports Outline Machine Learning Study Results from Moulay Ismail University (Ma chine Learning-based Predicting of Pcmintegrated Building Thermal Performance: an Application Under Various Weather Conditions In Morocco)

    84-85页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News-A new study on Machine Learning is now available. According to news originatingfrom Meknes, Morocco, by NewsRx corres pondents, research stated, "As is now stands, the buildingsector is among the ‘ big three' significant energy consumer and greenhouse gas contributor in the wor ld.Consequently, there has been a growing movement towards the development and adoption of renewableenergy sources, energy efficient measures and energy manag ement strategies."Our news journalists obtained a quote from the research from Moulay Ismail Unive rsity, "Phase changematerials (PCMs) are considered as an alluring option for e nergy efficiency measures in building. In thispaper, a case study building, bas ed on typical residential construction in Morocco, was selected to assessthe po tential energy savings of adding PCM to the roof for twenty-four cities using th e dynamic simulationtool, TRNSYS. Thereafter, thirteen machine learning techniq ues, including ANN, DT, SVM, ELM, GB,RF, TB, GLRM, GPR, LR, GAM, KRRM and LRR w ere assessed for predicting the hourly heating, cooling,and total energy consum ptions. The models were trained and tested on a dataset that was gathered fromt he simulations in twenty-four locations in Morocco. The outdoor dry-bulb tempera ture, the relativehumidity, the wind velocity, the wind direction, and the tota l solar radiation were considered as the keyfeatures. The obtained results reve aled that using PCM, can effectively lower the total energy demandfor all the c ities under study, with exception for very cold climate given by Ifrane and Mide lt, where theannual total energy consumption shows an increasing trend."
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    Findings from Tsinghua University in Machine Learning Reported (Integrating Fric tion Noise for In-Situ Monitoring of Polymer Wear Performance: A Machine Learnin g Approach in Tribology)

    85-85页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News-Research findings on artificial intell igence are discussed in a new report. Accordingto news originating from Tsinghu a University by NewsRx editors, the research stated, "Friction and wearbetween mating surfaces significantly affect the efficiency and performance of mechanica l systems."Our news correspondents obtained a quote from the research from Tsinghua Univers ity: "Traditionaltribological research relies on post-observation methods, limi ting the understanding of dynamic frictionbehavior. In contrast, in-situ monito ring provides real-time insights into evolving friction dynamics. Thisstudy emp loys machine learning to monitor polymer wear performance through friction noise . Thepredictive accuracy of various machine learning methods, including Extreme ly Randomized Trees, Gradient-Boosting Decision Trees, AdaBoost, LightGBM, Deep Forest, and Deep Neural Networks, is compared forwear type classification."
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    Charles Sturt University Reports Findings in Artificial Intelligence (Gender and Ethnicity Bias of Text-to-Image Generative Artificial Intelligence in Medical I maging, Part 1: Preliminary Evaluation)

    86-86页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News-New research on Artificial Intelligenc e is the subject of a report. According to newsreporting out of Wagga Wagga, Au stralia, by NewsRx editors, research stated, "Generative artificialintelligence (AI) text-to-image production could reinforce or amplify gender and ethnicity b iases. Severaltext-to-image generative AI tools are used for producing images t hat represent the medical imagingprofessions."Our news journalists obtained a quote from the research from Charles Sturt Unive rsity, "White malestereotyping and masculine cultures can dissuade women and et hnically divergent people from being drawninto a profession. In March 2024, DAL L-E 3, Firefly 2, Stable Diffusion 2.1, and Midjourney 5.2 wereutilized to gene rate a series of individual and group images of medical imaging professionals: r adiologist,nuclear medicine physician, radiographer, and nuclear medicine techn ologist. Multiple iterations of imageswere generated using a variety of prompts . Collectively, 184 images were produced for evaluation of 391characters. All i mages were independently analyzed by 3 reviewers for apparent gender and skin to ne.Collectively (individual and group characters) ( = 391), 60.6% were male and 87.7% were of a light skintone. DALL-E 3 (65.6% ), Midjourney 5.2 (76.7%), and Stable Diffusion 2.1 (56.2% ) had a statisticallyhigher representation of men than Firefly 2 (42.9% ) (<0.0001). With Firefly 2, 70.3% of charac ters hadlight skin tones, which was statistically lower (<0.0001) than for Stable Diffusion 2.1 (84.8%), Midjourney5.2 (100 %), and DALL-E 3 (94.8%). Overall, image quality metri cs were average or better in 87.2% forDALL-E 3 and 86.2% for Midjourney 5.2, whereas 50.9% were inadequate or poor for Fire fly 2 and 86.0% for Stable Diffusion 2.1. Generative AI text-to-im age generation using DALL-E 3 via GPT-4 has thebest overall quality compared wi th Firefly 2, Midjourney 5.2, and Stable Diffusion 2.1."
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