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    Tongji University Reports Findings in Chronic Obstructive Pul- monary Disease (A machine learning model for predicting acute exacerbation of in-home chronic obstructive pulmonary disease pa- tients)

    29-30页
    查看更多>>摘要：New research on Lung Diseases and Conditions - Chronic Obstructive Pulmonary Disease is the subject of a report. According to news reporting originating from Shanghai, People's Republic of China, by NewsRx correspondents, research stated, "This study utilized intelligent devices to remotely monitor patients with chronic obstructive pulmonary disease (COPD), aiming to construct and evaluate machine learning (ML) models that predict the probability of acute exacerbations of COPD (AECOPD). Patients diagnosed with COPD Group C/D at our hospital between March 2019 and June 2021 were enrolled in this study." Our news editors obtained a quote from the research from Tongji University, "The diagnosis of COPD Group C/D and AECOPD was based on the GOLD 2018 guidelines. We developed a series of machine learning (ML)-based models, including XGBoost, LightGBM, and CatBoost, to predict AECOPD events. These models utilized data collected from portable spirometers and electronic stethoscopes within a five- day time window. The area under the ROC curve (AUC) was used to assess the effectiveness of the models. A total of 66 patients were enrolled in COPD groups C/D, with 32 in group C and 34 in group D. Using observational data within a five-day time window, the ML models effectively predict AECOPD events, achieving high AUC scores. Among these models, the CatBoost model exhibited superior performance, boasting the highest AUC score (0.9721, 95 % CI: 0.9623-0.9810). Notably, the boosting tree methods significantly outperformed the time-series based methods, thanks to our feature engineering efforts. A post-hoc analysis of the CatBoost model reveals that features extracted from the electronic stethoscope (e.g., max/min vibration energy) hold more importance than those from the portable spirometer. The tree-based boosting models prove to be effective in predicting AECOPD events in our study."
      原文链接:
    • NETL
    • NSTL

    Laboratory of Biomedical Physics Reports Findings in Artificial In- telligence [Statistical analysis and generative Artificial Intelligence (AI) for assessing pain experience, pain-induced disability, and qual- ity of life in Parkinson's disease ...]

    30-31页
    查看更多>>摘要：New research on Artificial Intelligence is the subject of a report. According to news reporting originating from Lecce, Italy, by NewsRx correspondents, research stated, "The Parkinson's Disease (PD) is a chronic neurodegenerative condition characterized by motor symptoms such as tremors, rigidity, and bradykinesia, which can significantly impact various aspects of daily life. Among these aspects, pain is a prominent element." Our news editors obtained a quote from the research from the Laboratory of Biomedical Physics, "Despite the widespread use of therapies aimed at improving symptoms and quality of life, effective pain management is essential to enhance the quality of life of individuals affected by this disease. However, a detailed understanding of the factors associated with pain in PD is still evolving. In this study, we examined the disability caused by pain and the pain experienced by PD patients using two validated ques- tionnaires, namely the Parkinson's Disease Questionnaire (PDQ) and the King's Parkinson's Disease Pain Questionnaire (KPPQ). Customized questions were also included to further explore the pain experience and management strategies adopted by PD patients. Through statistical analysis, we explored the rela- tionships between questionnaire scores, socio-demographic data, and other relevant variables. Additionally, generative Artificial Intelligence (AI) was employed to gain a deeper understanding of patient responses. The results indicate the extent and impact of pain in PD and provide valuable insights for more targeted and personalized management."
      原文链接:
    • NETL
    • NSTL

    Findings in Support Vector Machines Reported from Chinese Academy of Sciences (Establishing a Soil Carbon Flux Monitoring System Based On Support Vector Machine and Xgboost)

    31-32页
    查看更多>>摘要：Current study results on Support Vector Machines have been published. According to news reporting out of Guizhou, People's Republic of China, by NewsRx editors, the research stated, "Soil carbon fluxes are pivotal indicators of climate impacts, yet field-level monitoring remains challenging. This study puts forth an innovative integrated framework coupling support vector machine (SVM) and XGBoost algorithms to enable automated, precise tracking of peat soil carbon dioxide emissions." Financial supporters for this research include Chinese Academy of Sciences, National Natural Science Foundation of China (NSFC). Our news journalists obtained a quote from the research from the Chinese Academy of Sciences, "The core methodology handles a multi-dimensional dataset encompassing 72-h flux measurements from 360 intact tropical peat cores under controlled moisture conditions spanning 30-85% water-filled pore space across intact, logged, and oil palm converted sites. Rigorous preprocessing via outlier elimination and miss- ing value imputation coupled with a tenfold cross-validation approach lays the robust analytical foundation. SVM first applies nonlinear transformation through Gaussian radial basis functions to classify complex soil respiration patterns. An optimized hyperplane decision boundary discretizes the high-dimensional space to separate classes. XGBoost subsequently constructs an ensemble of weighted decision trees targeting residual errors to incrementally boost predictions over 500 iterations. The integrated framework combines SVM and XGBoost outputs using performance-based weighting. This allows efficiently mapping intricate moisture, temperature, oxygen availability, microbial activity, and land use effects on peat soil carbon diox- ide production and emission dynamics. Integrated predictions leverage complementary strengths. Peaking at 94.4% accuracy, 92% precision, 91% recall and 0.3 RMSE, SVM with XGBoost decisively surpasses neural networks, LSTM, gradient boosting and regression trees, proving optimized encoding of intricate moisture, texture and land use effects on soil respiration. Clustered data representations confirm feasibility of mapping complex emission behaviors across intact and drained sites. Overall, the dual framework deliv- ers a precise, automated system to unlock new frontiers in responsive soil carbon monitoring and modeling at scale."
      原文链接:
    • NETL
    • NSTL
    • Springer Nature

    Data from East China Normal University Update Knowledge in Arti- ficial Intelligence (Teachers' AI-TPACK: Exploring the Relationship between Knowledge Elements)

    32-33页
    查看更多>>摘要：Data detailed on artificial intelligence have been presented. According to news origi- nating from Shanghai, People's Republic of China, by NewsRx editors, the research stated, "The profound impact of artificial intelligence (AI) on the modes of teaching and learning necessitates a reexamination of the interrelationships among technology, pedagogy, and subject matter. Given this context, we endeavor to construct a framework for integrating the Technological Pedagogical Content Knowledge of Artificial Intelligence Technology (Artificial Intelligence-Technological Pedagogical Content Knowledge, AI-TPACK) aimed at elucidating the complex interrelations and synergistic effects of AI technology, pedagogical meth- ods, and subject-specific content in the field of education." The news editors obtained a quote from the research from East China Normal University: "The AI- TPACK framework comprises seven components: Pedagogical Knowledge (PK), Content Knowledge (CK), AI-Technological Knowledge (AI-TK), Pedagogical Content Knowledge (PCK), AI-Technological Pedagog- ical Knowledge (AI-TCK), AI-Technological Content Knowledge (AI-TPK), and AI-TPACK itself. We developed an effective structural equation modeling (SEM) approach to explore the relationships among teachers' AI-TPACK knowledge elements through the utilization of exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). The result showed that six knowledge elements all serve as predictive factors for AI-TPACK variables. However, different knowledge elements showed varying levels of explana- tory power in relation to teachers' AI-TPACK. The influence of core knowledge elements (PK, CK, and AI-TK) on AI-TPACK is indirect, mediated by composite knowledge elements (PCK, AI-TCK, and AI- TPK), each playing unique roles. Non-technical knowledge elements have significantly lower explanatory power for teachers of AI-TPACK compared to knowledge elements related to technology. Notably, con- tent knowledge (C) diminishes the explanatory power of PCK and AI-TCK. This study investigates the relationships within the AI-TPACK framework and its constituent knowledge elements."
      原文链接:
    • NETL
    • NSTL
    • Mdpi

    Researchers from Beijing Institute of Technology Report Re- cent Findings in Robotics and Automation (Lcpr: a Multi-scale Attention-based Lidar-camera Fusion Network for Place Recogni- tion)

    33-34页
    查看更多>>摘要：Investigators publish new report on Robotics - Robotics and Automation. According to news reporting out of Beijing, People's Republic of China, by NewsRx editors, research stated, "Place recognition is one of the most crucial modules for autonomous vehicles to identify places that were previously visited in GPS-invalid environments. Sensor fusion is considered an effective method to overcome the weaknesses of individual sensors." Financial support for this research came from National Natural Science Foundation of China (NSFC). Our news journalists obtained a quote from the research from the Beijing Institute of Technology, "In recent years, multimodal place recognition fusing information from multiple sensors has gathered increasing attention. However, most existing multimodal place recognition methods only use limited field-of-view camera images, which leads to an imbalance between features from different modalities and limits the effectiveness of sensor fusion. In this letter, we present a novel neural network named LCPR for robust multimodal place recognition, which fuses LiDAR point clouds with multi-view RGB images to generate discriminative and yaw-rotation invariant representations of the environment. A multi-scale attention-based fusion module is proposed to fully exploit the panoramic views from different modalities of the environment and their correlations."
      原文链接:
    • NETL
    • NSTL
    • IEEE

    Universidad Tecnologica de Bolivar Reports Findings in Artificial Intelligence (Assessing Fuchs Corneal Endothelial Dystrophy Us- ing Artificial Intelligence-Derived Morphometric Parameters From Specular Microscopy Images)

    34-35页
    查看更多>>摘要：New research on Artificial Intelligence is the subject of a report. According to news reporting from Cartagena, Colombia, by NewsRx journalists, research stated, "The aim of this study was to evaluate the efficacy of artificial intelligence-derived morphometric parameters in characterizing Fuchs corneal endothelial dystrophy (FECD) from specular microscopy images. This cross-sectional study recruited patients diagnosed with FECD, who underwent ophthalmologic evaluations, including slit-lamp examinations and corneal endothelial assessments using specular microscopy." Financial support for this research came from Departamento Administrativo de Ciencia, TecnologA-a e InnovaciAn. The news correspondents obtained a quote from the research from Universidad Tecnologica de Boli- var, "The modified Krachmer grading scale was used for clinical FECD classification. The images were processed using a convolutional neural network for segmentation and morphometric parameter estimation, including effective endothelial cell density, guttae area ratio, coefficient of variation of size, and hexagonal- ity. A mixed-effects model was used to assess relationships between the FECD clinical classification and measured parameters. Of 52 patients (104 eyes) recruited, 76 eyes were analyzed because of the exclusion of 26 eyes for poor quality retroillumination photographs. The study revealed significant discrepancies between artificial intelligence-based and built-in microscope software cell density measurements (1322 ? 489 cells/mm 2 vs. 2216 ? 509 cells/mm 2 , P＜0.001). In the central region, guttae area ratio showed the strongest correlation with modified Krachmer grades (0.60, P＜0.001). In peripheral areas, only guttae area ratio in the inferior region exhibited a marginally significant positive correlation (0.29, P＜0.05). This study confirms the utility of CNNs for precise FECD evaluation through specular microscopy. Guttae area ratio emerges as a compelling morphometric parameter aligning closely with modified Krachmer clinical grading."
      原文链接:
    • NETL
    • NSTL

    Data on Robotics Reported by Nikolaos Evangelopoulos and Col- leagues (Minimally invasive sacrocolpopexy: efficiency of robotic assistance compared to standard laparoscopy)

    35-36页
    查看更多>>摘要：New research on Robotics is the subject of a report. According to news reporting originating from Lausanne, Switzerland, by NewsRx correspondents, research stated, "Minimally invasive abdominal sacrocolpopexy (SC) is the treatment of choice for symptomatic, high-grade, apical or multi- compartmental pelvic organ prolapse (POP), in terms of anatomical correction and treatment durability. Robot-assisted sacrocolpopexy (RASC) could be an attractive alternative to the gold standard laparoscopic sacrocolpopexy (LSC), for its ergonomic advantages in such a technically demanding procedure." Financial support for this research came from University of Lausanne. Our news editors obtained a quote from the research, "However, it has not yet proven its superiority, consequently raising cost-effectiveness issues. Our primary objective was to assess if RASC can achieve better overall operative time (OOT) over LSC, with at least equivalent perioperative results. This was a single-center retrospective study including 100 patients (58 consecutive RASC cases and 42 LSC within the same time-period), with primary endpoint the OOT in both groups. Secondary results included complication rate, hospital stay, short-term anatomic results and OOT within and beyond the RASC learning curve. A multivariate linear regression was carried out for our primary outcome. The groups had comparable characteristics, except for BMI, which was lower in RASC group. The mean OOT was significantly lower in the RASC group (188 vs. 217 min, p 0.01), even after adjusting for possible confounders. Short-term anatomic results, complication rate, and blood loss were similar in the two groups. Mean hospital stay was significantly longer in the RASC group. Average RASC OOT was significantly shorter after the first 20 cases realized."
      原文链接:
    • NETL
    • NSTL
    • Springer Nature

    Findings from Kunming University Broaden Understanding of Ma- chine Learning (Machine Learning Models To Predict the Residual Tensile Strength of Glass Fiber Reinforced Polymer Bars In Strong Alkaline Environments: a Comparative Study)

    36-37页
    查看更多>>摘要：Investigators publish new report on Machine Learning. According to news reporting from Yunnan, People's Republic of China, by NewsRx journalists, research stated, "The long-term durability of glass fiber reinforced polymers (GFRPs) in strong alkaline environments is of utmost importance in marine infrastructure construction. The residual tensile strength, as one of the important durability indicators, can be characterized by the tensile strength retention (TSR)." Funders for this research include National Natural Science Foundation of China (NSFC), Applied Basic Research Foundation of Yunnan Province, China. The news correspondents obtained a quote from the research from Kunming University, "However, accurate prediction of the TSR is a challenging task. Therefore, the main objective of this study is to develop a generalized, accurate, and optimized TSR prediction model from machine learning (ML) perspective. To this aim, seven machine learning models were developed using the diameter of the bar (db), the volume fraction (Vf) of the E-glass fibers, the pH of the alkaline solution, the conditioning temperature (temp) and the duration of immersion (T) as input variables. A database containing 150 sets of samples, divided into training and testing sets, was created for model building and comparison. Evaluated against three performance evaluation metrics (including RMSE, R2, and VAF) and the Taylor diagram, the final generalization performance of the models was found to be the extreme gradient boosting (XGBoost), long short-term memory (LSTM), support vector regression (SVR), random forest (RF), extreme learning machine (ELM), backpropagation neural network (BPNN), and generalized additive model (GAM) from highest to lowest. In addition, the relative sensitivity of the five input variables was assessed by the one variable at a time (OVAAT) method, and pH and temp were identified as the top two most significant variables in TSR prediction. This study also explored the effect of the training set/test set division ratio on the model, an aspect that has not been investigated in previous studies, and identified 8:2 as the optimal division ratio."
      原文链接:
    • NETL
    • NSTL
    • Elsevier

    Investigators at Xiangtan University Report Findings in Nanopar- ticles (Machine Learning Guided Hydrothermal Synthesis of Ther- mochromic Vo2 Nanoparticles)

    37-38页
    查看更多>>摘要：2024 FEB 22 (NewsRx) – By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Research findings on Nanotechnology - Nanoparticles are discussed in a new report. According to news originating from Hunan, People's Republic of China, by NewsRx correspondents, research stated, "Vanadium dioxide (VO2) is a promising material for energy-saving smart windows due to its reversible metal-to-insulator transition near room temperature, concomitantly with a structural phase transition be- tween monoclinic VO2(M) phase and rutile VO2® phase. However, the fact that VO2 has a complex crystalline phase makes its reliable synthesis an obstacle to its practical application." Funders for this research include National Natural Science Foundation of China (NSFC), Natural Science Foundation of Hunan Province, Hunan Provincial Education Department, Hunan Provincial Innovation Foundation for Postgraduate. Our news journalists obtained a quote from the research from Xiangtan University, "Machine learning (ML), a specific subset of artificial intelligence, can be utilized to generate virtual representations of experimental conditions and outcomes for the purpose of predicting experiments. Therefore, in the paper, four machine learning models were trained to perform optimization of the VO2 hydrothermal synthesis. A random forest model achieved a classification ac-curacy of 87.27%. The synthetic parameter space was explored to filter combinations with a synthetic probability above 90%. Random forest models were used to guide the experimental synthesis, and the obtained products were characterized using X-ray diffraction, scanning electron microscopy, X-ray photoelectron spectroscopy, and differential scanning calorimetry." According to the news editors, the research concluded: "The results showed that phase-pure VO2(B) and VO2(M) were successfully synthesized, demonstrating the effectiveness of machine learning in optimiz- ing material synthesis, alleviating the stochasticity of material synthesis caused by the control of synthesis conditions, and promoting the application research of VO2 materials."
      原文链接:
    • NETL
    • NSTL
    • Elsevier

    University of Pittsburgh School of Medicine Reports Findings in Robotics (Optimal Assessment For Anterior Talofibular Ligament Injury Utilizing Stress Ultrasound Entails Internal Rotation During Plantarflexion)

    38-39页
    查看更多>>摘要：New research on Robotics is the subject of a report. According to news reporting from Pittsburgh, Pennsylvania, by NewsRx journalists, research stated, "An optimal load and ankle position for stress ultrasound of injured anterior talofibular ligament (ATFL) is unknown. Objectives of this study were to compare stress ultrasound and ankle kinematics from a 6 degree-of-freedom (6-DOF) robotic testing system as a reference standard for evaluation of injured ATFL and suggest cut-off values for ultrasound diagnosis." The news correspondents obtained a quote from the research from the University of Pittsburgh School of Medicine, "Ten fresh-frozen human cadaveric ankles were used. Loads and ankle positions examined by 6-DOF robotic testing system were: 40 N anterior load, 1.7 Nm inversion and 1.7 Nm internal rotation torques at 30° plantarflexion, 15° plantarflexion, and 0° plantarflexion. Bony translations were measured by ultrasound and robotic testing system under the above conditions. After measuring intact ankle, ATFL was transected at its fibular attachment under arthroscopy. Correlations between ultrasound and robotic testing system were calculated with Pearson correlation coefficients. Paired t-tests were performed for comparison of ultrasound measurements of translation between intact and transected ATFL, and unloaded and loaded conditions in transected ATFL. Good agreement between ultrasound measurement and that of robotic testing system was found only in internal rotation at 30° plantarflexion (ICC=0.77; 95% Confidence Interval 0.27-0.94). At 30° plantarflexion, significant differences in ultrasound measurements of translation between intact and transected ATFL (p ＜0.01) were found in response to 1.7 Nm internal rotation torque, and non-stress and stress with internal rotation (p ＜0.01) with mean differences of 2.4 mm and 1.9 mm respectively."
      原文链接:
    • NETL
    • NSTL