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    Study Results from Aston University in the Area of Machine Learning Reported (En ergy Performance of Building Refurbishments: Predictive and Prescriptive Ai-base d Machine Learning Approaches)

    96-97页
    查看更多>>摘要：Fresh data on Machine Learning are pre sented in a new report. According to news reporting originating from Birmingham, United Kingdom, by NewsRx correspondents, research stated, "The energy performa nce (EP) of buildings is critical for European governments to meet their decarbo nization targets by 2050. In the context of European Union (EU) policies, which subsidize citizen-led building renovations, it is imperative to ascertain the ef ficacy of these renovations in significantly enhancing EP." Our news editors obtained a quote from the research from Aston University, "This study relies on six AI-based machine learning (ML) algorithms to identify key p redictors and prescribe measures for enhancing post-renovation EP in building re furbishments. The gradient boosting model outperforms the other ML models with a n accuracy rate of 84.34 % as the most effective predictive model. Moreover, an analysis of numerous predictors in the experiment showed that impl ementing modern energy-efficient heating systems, optimizing dwelling characteri stics, regular maintenance, investing in high-performance insulation materials, and understanding the dynamics of the occupants were relevant prescriptions for efficient energy-saving strategies."
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    First Affiliated Hospital Reports Findings in Personalized Medicine (Interpretab le machine learning model predicting immune checkpoint inhibitor-induced hypothy roidism: A retrospective cohort study)

    97-98页
    查看更多>>摘要：New research on Drugs and Therapies-Personalized Medicine is the subject of a report. According to news reporting or iginating in Zhejiang, People's Republic of China, by NewsRx journalists, resear ch stated, "Hypothyroidism is a known adverse event associated with the use of i mmune checkpoint inhibitors (ICIs) in cancer treatment. This study aimed to deve lop an interpretable machine learning (ML) model for individualized prediction o f hypothyroidism in patients treated with ICIs." Financial support for this research came from Medical Science and Technology Pro ject of Zhejiang Province. The news reporters obtained a quote from the research from First Affiliated Hosp ital, "The retrospective cohort of patients treated with ICIs was from the First Affiliated Hospital of Ningbo University. ML methods applied include logistic r egression (LR), random forest classifier (RFC), support vector machine (SVM), an d extreme gradient boosting (XGBoost). The area under the receiver-operating cha racteristic curve (AUC) was the main evaluation metric used. Furthermore, the Sh apley additive explanation (SHAP) was utilized to interpret the outcomes of the prediction model. A total of 458 patients were included in the study, with 59 pa tients (12.88%) observed to have developed hypothyroidism. Among th e models utilized, XGBoost exhibited the highest predictive capability (AUC = 0. 833). The Delong test and calibration curve indicated that XGBoost significantly outperformed the other models in prediction. The SHAP method revealed that thyr oid-stimulating hormone (TSH) was the most influential predictor variable. The d eveloped interpretable ML model holds potential for predicting the likelihood of hypothyroidism following ICI treatment in patients."
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    Studies from Shenzhen University Yield New Data on Machine Learning (Machine Lea rning-based Causal Inference for Evaluating Intervention In Travel Behaviour Res earch: a Difference-indifferences Framework)

    98-99页
    查看更多>>摘要：Fresh data on Machine Learning are pre sented in a new report. According to news reporting originating in Shenzhen, Peo ple's Republic of China, by NewsRx journalists, research stated, "Causal inferen ce with the difference-in-differences (DID) framework is popular in identifying causal effects with observational data and has started to be applied in recent t ravel behaviour studies. Most relevant transportation research adopts the conven tional linear parametric DID model, which is known to be inflexible and restrict ive." Funders for this research include National Natural Science Foundation of China ( NSFC), Guangdong Basic and Applied Basic Research Foundation, Key Project of Nat ural Science Foundation of Shenzhen, Key Technology Project of the Commission of Science and Technology of Shenzhen, Tencent-Rhino Bird Fund, Hong Kong Research Grants Council.
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    King's College Researcher Describes Advances in Machine Learning (New horizons i n prediction modelling using machine learning in older people's healthcare resea rch)

    99-100页
    查看更多>>摘要：Data detailed on artificial intelligen ce have been presented. According to news originating from London, United Kingdo m, by NewsRx editors, the research stated, "Machine learning (ML) and prediction modelling have become increasingly influential in healthcare, providing critica l insights and supporting clinical decisions, particularly in the age of big dat a." Funders for this research include National Institute For Health Research (Nihr) Biomedical Research Centre At South London; Maudsley Nhs Foundation Trust And Ki ng's College London. Our news editors obtained a quote from the research from King's College: "This p aper serves as an introductory guide for health researchers and readers interest ed in prediction modelling and explores how these technologies support clinical decisions, particularly with big data, and covers all aspects of the development , assessment and reporting of a model using ML. The paper starts with the import ance of prediction modelling for precision medicine. It outlines different types of prediction and machine learning approaches, including supervised, unsupervis ed and semi-supervised learning, and provides an overview of popular algorithms for various outcomes and settings. It also introduces key theoretical ML concept s. The importance of data quality, preprocessing and unbiased model performance evaluation is highlighted. Concepts of apparent, internal and external validatio n will be introduced along with metrics for discrimination and calibration for d ifferent types of outcomes."
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    Studies from University of Wisconsin in the Area of Robotics and Automation Desc ribed (Using a Distance Sensor To Detect Deviations In a Planar Surface)

    100-101页
    查看更多>>摘要：Current study results on Robotics-Ro botics and Automation have been published. According to news originating from Ma dison, Wisconsin, by NewsRx correspondents, research stated, "We investigate met hods for determining if a planar surface contains geometric deviations (e.g., pr otrusions, objects, divots, or cliffs) using only an instantaneous measurement f rom a miniature optical time-of-flight sensor. The key to our method is to utili ze the entirety of information encoded in raw time-of-flight data captured by of f-the-shelf distance sensors." Financial supporters for this research include Los Alamos National Laboratory, U nited States Department of Energy (DOE), NSF-Office of the Director (OD), Nati onal Science Foundation (NSF), Office of Naval Research.
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    New Robotics Findings from Southeast University Reported (Multiview Registratio n of Partially Overlapping Point Clouds for Robotic Manipulation)

    101-102页
    查看更多>>摘要：Investigators publish new report on Ro botics. According to news reporting from Nanjing, People's Republic of China, by NewsRx journalists, research stated, "Point cloud registration is a fundamental task in intelligent robots, aiming to achieve globally consistent geometric str uctures and providing data support for robotic manipulation. Due to the limited view of measurement devices, it is necessary to collect point clouds from multip le views to construct a complete model." Funders for this research include National Natural Science Foundation of China ( NSFC), Ministry of Industry and Information Technology Basic Research Project. The news correspondents obtained a quote from the research from Southeast Univer sity, "Previous multi-view registration methods rely on sufficient overlap and r egistering all pairs of point clouds, resulting in slow convergence and high cum ulative errors. To solve these challenges, we present a multi-view registration method based on the point-to-plane model and pose graph. We introduce a robust k ernel into the objective function to diminish registration errors caused by mism atched points. Additionally, an enhanced Euclidean clustering method is proposed for extracting object point clouds. Subsequently, by establishing pose constrai nts on non-adjacent frames of point clouds, the cumulative error is reduced, ach ieving global optimization based on the pose graph. Experimental results demonst rate the robustness of our method with respect to overlap ratios, successfully r egistering point clouds with overlap ratio exceeding 30$% ."
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    Data on Robotics Described by Researchers at Shanghai University (Multi-objectiv e Teaching-learning-based Optimizer for a Multiweeding Robot Task Assignment Pr oblem)

    102-103页
    查看更多>>摘要：2024 OCT 08 (NewsRx)-By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-New research on Robotics is the subject of a repo rt. According to news reporting from Shanghai, People's Republic of China, by Ne wsRx journalists, research stated, "With the emergence of the artificial intelli gence era, all kinds of robots are traditionally used in agricultural production . However, studies concerning the robot task assignment problem in the agricultu re field, which is closely related to the cost and efficiency of a smart farm, a re limited." Financial support for this research came from National Natural Science Foundatio n of China (NSFC). The news correspondents obtained a quote from the research from Shanghai Univers ity, "Therefore, a Multi-Weeding Robot Task Assignment (MWRTA) problem is addres sed in this paper to minimize the maximum completion time and residual herbicide . A mathematical model is set up, and a Multi-Objective Teaching-Learning-Based Optimization (MOTLBO) algorithm is presented to solve the problem. In the MOTLBO algorithm, a heuristic-based initialization comprising an improved Nawaz Enscor e, and Ham (NEH) heuristic and maximum load-based heuristic is used to generate an initial population with a high level of quality and diversity. An effective t eaching-learning-based optimization process is designed with a dynamic grouping mechanism and a redefined individual updating rule. A multi-neighborhood-based l ocal search strategy is provided to balance the exploitation and exploration of the algorithm. Finally, a comprehensive experiment is conducted to compare the p roposed algorithm with several state-of-the-art algorithms in the literature."
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    Reports Outline Artificial Intelligence Findings from Chinese Academy of Science s (Functional Tactile Sensor Based On Arrayed Triboelectric Nanogenerators)

    103-104页
    查看更多>>摘要：New research on Artificial Intelligenc e is the subject of a report. According to news reporting out of Beijing, People 's Republic of China, by NewsRx editors, research stated, "In the era of Interne t of Things (IoT) and Artificial Intelligence (AI), sensors have become an integ ral part of intelligent systems. Although the traditional sensing technology is very mature in long-term development, there are remaining defects and limitation s that make it difficult to meet the growing demands of current applications, su ch as high-sensitivity detection and self-supplied sensing." Financial supporters for this research include National Modern Agricultural Indu stry Technology System Project, Fundamental Research Funds for the Central Unive rsities, National Natural Science Foundation of China (NSFC), Hubei Provincial N atural Science Foundation General Project, Beijing Natural Science Foundation.
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    Department of General Surgery Reports Findings in Pancreatitis (Machine learning predicts acute respiratory failure in pancreatitis patients: A retrospective st udy)

    104-105页
    查看更多>>摘要：New research on Digestive System Disea ses and Conditions-Pancreatitis is the subject of a report. According to news reporting from Gansu, People's Republic of China, by NewsRx journalists, researc h stated, "The purpose of the research is to design an algorithm to predict the occurrence of acute respiratory failure (ARF) in patients with acute pancreatiti s (AP). We collected data on patients with AP in the Medical Information Mart fo r Intensive Care IV database." The news correspondents obtained a quote from the research from the Department o f General Surgery, "The enrolled observations were randomly divided into a 70 % training cohort and a 30 % validation cohort, and the observations in the training cohort were divided into ARF and non-ARF groups. Feature engine ering was conducted using random forest (RF) and least absolute shrinkage and se lection operator (LASSO) methods in the training cohort. The model building incl uded logistic regression (LR), decision tree (DT), k-nearest neighbours (KNN), n aive bayes (NB) and extreme gradient boosting (XGBoost). Parameters for model ev aluation include receiver operating characteristic (ROC) curve, precision-recall curve (PRC), calibration curves, positive predictive value (PPV), negative pred ictive value (NPV), true positive rate (TPR), true negative rate (TNR), accuracy (ACC) and F1 score. Among 4527 patients, 445 patients (9.8 %) expe rienced ARF. Ca, ALB, GLR, WBC, AG and BUN have been included in the prediction model as features for predicting ARF. The AUC of XGBoost were 0.86 (95 % CI 0.84-0.88) and 0.87 (95 %CI 0.84-0.90) in the training and valid ation cohorts. In the training cohort, XGBoost demonstrates a true positive rate (TPR) of 0.662, a true negative rate (TNR) of 0.884, a positive predictive valu e (PPV) of 0.380, a negative predictive value (NPV) of 0.960, an accuracy (ACC) of 0.862, and an F1 score of 0.483. In the validation cohort, XGBoost shows a TP R of 0.620, a TNR of 0.895, a PPV of 0.399, an NPV of 0.955, an ACC of 0.867, an d an F1 score of 0.486."
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    Studies in the Area of Robotics Reported from University of Manitoba (Articulati on Work for Supporting the Values of Students Attending Class Via Telepresence R obots)

    105-106页
    查看更多>>摘要：Current study results on Robotics have been published. According to news reporting originating in Winnipeg, Canada, by NewsRx journalists, research stated, "Robotic Telepresence (TR) is a promising medium for providing classroom access for students who are unable to attend clas ses in-person. While existing research has focused on TR's usability, adoption, and embodiment, there is a need for research focusing on how TR supports key use r values-like identity, privacy, and courtesy-in educational contexts." Financial support for this research came from National Science Foudation (NSF). The news reporters obtained a quote from the research from the University of Man itoba, "To bridge this gap, we engaged 22 university students in a field study u sing Beam telepresence robots, which enabled us to discern the key manifestation s of these three values in classroom human-robot interactions. We also identifie d articulation work improvised by remote students to maintain these values. Base d on our findings, we propose recommendations for use that can support these val ues and offer design recommendations for future telepresence robots."
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