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    Tongji University Reports Findings in Artificial Intelligence (Hydrogen-Bonded O rganic Frameworks for Antibiotic Fluorescent Sensing Artificial Intelligence-Enh anced Anticounterfeiting)

    68-68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 out of Shanghai, Peopl e’s Republic of China, by NewsRx editors, research stated, “The paramount import ance of anticounterfeiting measures in safeguarding consumers from counterfeit p roducts lies in their ability to ensure product safety and reliability. Advanced luminescent anticounterfeiting materials, particularly those responsive to mult iple stimuli, afford a dynamic and multilayered security assurance.” Our news journalists obtained a quote from the research from Tongji University, “This study presents the synthesis of a novel material, Eu/Tb@GC-3, via postsynt hetic modification, which exhibits notable photoluminescent properties with emis sion at 544 and 614 nm. The material demonstrates high selectivity and sensitivi ty in detecting Nitrofural and Enrofloxacin, with limits of detection at 0.0122 and 0.0280 mM, respectively. Furthermore, multistimulus responsive luminescent f ibers and inks were developed, facilitating intelligent anticounterfeiting label s.”
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    Researchers from Petrozavodsk State University Publish Findings in Machine Learn ing (Assessment of Stress Level with Help of 'Smart Clothing' Sensors, Heart Rat e Variability-Based Markers and Machine Learning Algorithms)

    68-69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 artificial intelligence. According to news reporting originating from Petrozavodsk, Russia, by NewsRx correspondents, research stated, “Physiological stress in healthy sub jects was assessed using heart rate (HR), monitored with the help of Hexoskin sm art garments.” The news reporters obtained a quote from the research from Petrozavodsk State Un iversity: “HRs were collected during daily life activities and in laboratory set tings during stress tests. Heart rate variability parameters were computed and r eferenced with expert levels of stress. The data were processed with the help of machine learning algorithms (Random Forest, CatBoost, XGB, LGBM, SVR).”
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    Kunming Medical University Reports Findings in Bioinformatics (Investigating the molecular mechanisms between type 1 diabetes and mild cognitive impairment usin g bioinformatics analysis, with a focus on immune response)

    69-70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 Biotechnology - Bioinf ormatics is the subject of a report. According to news reporting from Kunming, P eople’s Republic of China, by NewsRx journalists, research stated, “The immune s ystem is involved in the development and progression of several diseases. Type 1 diabetes mellitus (T1DM), an autoimmune disorder, influences the progression of several other conditions; however, the link between T1DM and mild cognitive imp airment (MCI) remains unclear.” The news correspondents obtained a quote from the research from Kunming Medical University, “This study investigated the underlying immune response mechanisms t hat contribute to the development and progression of T1DM and MCI. Microarray da tasets for MCI (GSE63060) and T1DM (GSE30208) were retrieved from the Gene Expre ssion Omnibus database. Differentially expressed genes (DEGs) were identified us ing the limma package. To explore the functional implications of these DEGs, Gen e Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analys es were conducted using Cluster- Profiler. Protein-protein interaction networks fo r the DEGs were constructed using the STRING database and visualized using Cytos cape. The Molecular Complex Detection algorithm was used to analyze DEGs. Immune cell infiltration in patients with T1DM and MCI was analyzed using the xCell me thod. Gene set enrichment analysis was used to gain in-depth insights into the f unctional characteristics of T1DM and MCI. Immune-related genes were obtained fr om the GeneCards and ImmPort databases. Machine learning algorithms were used to identify potential hub genes associated with immunity for T1DM and MCI diagnosi s, and the diagnostic value was assessed using the receiver operating characteri stic curve.The identified genes were validated using quantitative polymerase ch ain reaction. In the T1DM and MCI datasets, 610 DEGs showed consistent trends, o f which 232 and 378 were upregulated and downregulated, respectively. Immune res ponse analysis revealed significant changes in the immune cells associated with MCI and T1DM. Using immune-related genes, DEGs, and machine learning techniques, we identified CD3D in the MCI and T1DM groups. We observed a gradual decline in the cognitive function of mice with T1DM as the disease progressed. CD3D expres sion increased with increasing disease severity; CD3D primarily affected CD4+ T cells. This study revealed a complex interaction between T1DM and MCI, providing novel insights into the intricate relationship between immune dysregulation and cognitive impairment and expanding our understanding of these two interconnecte d disorders.”
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    Lanzhou University Second Hospital Reports Findings in Machine Learning (Study o n medical dispute prediction model and its clinical-application effectiveness ba sed on machine learning)

    70-71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 from Lanzhou, People’s Republ ic of China, by NewsRx journalists, research stated, “Medical dispute is a globa l public health issue, which has been garnering increasing attention. In this st udy, we used machine learning (ML) method to establish a dispute prediction mode l and explored the clinical-application efficiency of this model in effectively reducing the occurrence of medical disputes.” The news correspondents obtained a quote from the research from Lanzhou Universi ty Second Hospital, “Retrospective study of All disputes filed by Gansu Medical Mediation Committee from 2019 to 2021 and patients with the same hospital level as that of the dispute group and hospitalization year were randomly selected as the control group in 1:1 ratio. SPSS software was used for univariate feature se lection of the 14 factors that may cause disputes, and factors with statistical differences were selected. The data were divided into training and test sets in a 7:3 ratio. Six ML models were selected, and Python was used to establish a dis pute prediction model. The area under the curve (AUC) of the receiver operating characteristic curve (ROC), sensitivity, specificity, accuracy, precision, avera ge precision (AP), and F1 score were used to characterize the fitting and accura cy of the models, while decision curve analysis (DCA) was used to evaluate their clinical utility. A total of 1189 patients in the dispute and control groups we re extracted. Following 11 influencing factors were selected: the inpatient depa rtment, doctor title, patient age, patient gender, patient occupation, payment m ethod, hospitalization days, hospitalization times, discharge method, blood tran sfusion volume, and hospitalization espenses. Compared to other models, the AUC (0.945, 95% CI 0.913-0.981), Sensitivity (0.887), Accuracy (0.887) , AP (0.834), and F1 score (0.880) of the random forest model were higher than t hose of other models, while the DCA curve indicated its high clinical benefits. Inpatient department, hospitalization expenses, and discharge type are the prima ry influencing factors of dispute.”
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    New Machine Learning Study Findings Reported from University of Cologne (Automat ed quality control of small animal MR neuroimaging data)

    71-72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 from Cologne, Germany, by Ne wsRx journalists, research stated, “MRI is a valuable tool for studying brain st ructure and function in animal and clinical studies. With the growth of public M RI repositories, access to data has finally become easier.” The news journalists obtained a quote from the research from University of Colog ne: “However, filtering large data sets for potential poor-quality outliers can be a challenge. We present AIDAqc, a machine learning-assisted automated Python- based command-line tool for small animal MRI quality assessment. Quality control features include signal-to-noise ratio (SNR), temporal SNR, and motion. All fea tures are automatically calculated and no regions of interest are needed. Automa ted outlier detection for a given dataset combines the interquartile range and t he machine learning methods one-class support vector machine, isolation forest, local outlier factor, and elliptic envelope. To evaluate the reliability of indi vidual quality control metrics, a simulation of noise (Gaussian, salt and pepper , speckle) and motion was performed. In outlier detection, single scans with ind uced artifacts were successfully identified by AIDAqc. AIDAqc was challenged in a large heterogeneous dataset collected from 19 international laboratories, incl uding data from mice, rats, rabbits, hamsters, and gerbils, obtained with differ ent hardware and at different field strengths. The results show that the manual inter-rater agreement (mean Fleiss Kappa score 0.17) is low when identifying poo r-quality data. A direct comparison of AIDAqc results, therefore, showed only lo w to moderate concordance.”
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    Study Findings on Intelligent Systems Are Outlined in Reports from Henan Univers ity (Cooperative Multi-agent Target Searching: a Deep Reinforcement Learning App roach Based On Parallel Hindsight Experience Replay)

    72-73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 Machine Learn ing - Intelligent Systems. According to news originating from Zhengzhou, People’ s Republic of China, by NewsRx correspondents, research stated, “Multi-agent mul ti-target search strategies can be utilized in complex scenarios such as post-di saster search and rescue by unmanned aerial vehicles. To solve the problem of fi xed target and trajectory, the current multi-agent multi-target search strategie s are mainly based on deep reinforcement learning (DRL).” Funders for this research include National Natural Science Foundation of China ( NSFC), Program for Science & Technology Development of Henan Provi nce, Young Elite Scientist Sponsorship Program by Henan Association for Science and Technology.
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    New Artificial Intelligence Study Results from East China University of Science and Technology Described (Artificial Intelligencemotivated In-situ Imaging for Visualization Investigation of Submicron Particles Deposition In ...)

    73-74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 Artificial Intell igence are discussed in a new report. According to news reporting originating fr om Shanghai, People’s Republic of China, by NewsRx correspondents, research stat ed, “This study delves into the intricate deposition dynamics of submicron parti cles within electric-flow coupled fields, underscoring the unique challenges pos ed by their minuscule size, aggregation tendencies, and biological reactivity. E mploying an operando investigation system that synergizes microfluidic technolog y with advanced micro-visualization techniques within a lab-on-a-chip framework enables a meticulous examination of the dynamic deposition phenomena.” Financial support for this research came from National Natural Science Foundatio n of China (NSFC).
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    Researcher from University of Sharjah Publishes Findings in Machine Learning (A Comparative Study of Pavement Roughness Prediction Models under Different Climat ic Conditions)

    74-75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 from the University of Sharj ah by NewsRx journalists, research stated, “Predicting the International Roughne ss Index (IRI) is crucial for maintaining road quality and ensuring the safety a nd comfort of road users. Accurate IRI predictions help in the timely identifica tion of road sections that require maintenance, thus preventing further deterior ation and reducing overall maintenance costs.” The news reporters obtained a quote from the research from University of Sharjah : “This study aims to develop robust predictive models for the IRI using advance d machine learning techniques across different climatic conditions. Data were so urced from the Ministry of Energy and Infrastructure in the UAE for localized co nditions coupled with the Long-Term Pavement Performance (LTPP) database for com parison and validation purposes. This study evaluates several machine learning m odels, including regression trees, support vector machines (SVMs), ensemble tree s, Gaussian process regression (GPR),artificial neural networks (ANNs), and ker nel-based methods. Among the models tested, GPR, particularly with rational quad ratic specifications, consistently demonstrated superior performance with the lo west Root Mean Square Error (RMSE) and highest R-squared values across all datas ets. Sensitivity analysis identified age, total pavement thickness, precipitatio n, temperature, and Annual Average Daily Truck Traffic (AADTT) as key factors in fluencing the IRI. The results indicate that pavement age and higher traffic loa ds significantly increase roughness, while thicker pavements contribute to smoot her surfaces. Climatic factors such as temperature and precipitation showed vary ing impacts depending on the regional conditions.”
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    Findings from Xi’an University of Science and Technology Reveals New Findings on Robotics (Ant Colony Optimization-based Method for Energy-efficient Cutting Tra jectory Planning In Axial Robotic Roadheader)

    75-76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 Robotics is now availab le. According to news originating from Xi’an, People’s Republic of China, by New sRx correspondents, research stated, “The traditional cutting trajectory of the axial robotic roadheader (spiral or reciprocating) is too simple to meet the req uirements of adaptive planning. We proposed a method for planning the cutting tr ajectory of an axial robot roadheader based on ant colony optimization, which en ables the machine to automatically adapt to the rock characteristics of the tunn el face, solves the problems of high energy consumption and low cutting efficien cy in existing methods.” Funders for this research include National Natural Science Foundation of China ( NSFC), Key R & D Project in Shaanxi.
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    Hebei North University Reports Findings in Pulmonary Embolism (Prediction of sho rt-term adverse clinical outcomes of acute pulmonary embolism using conventional machine learning and deep Learning based on CTPA images)

    76-77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 Lung Diseases and Cond itions - Pulmonary Embolism is the subject of a report. According to news report ing out of Hebei, People’s Republic of China, by NewsRx editors, research stated , “To explore the predictive value of traditional machine learning (ML) and deep learning (DL) algorithms based on computed tomography pulmonary angiography (CT PA) images for short-term adverse outcomes in patients with acute pulmonary embo lism (APE). This retrospective study enrolled 132 patients with APE confirmed by CTPA.” Our news journalists obtained a quote from the research from Hebei North Univers ity, “Thrombus segmentation and texture feature extraction was performed using 3 D-Slicer software. The least absolute shrinkage and selection operator (LASSO) a lgorithm was used for feature dimensionality reduction and selection, with optim al l values determined using leave-one-fold cross-validation to identify texture features with non-zero coefficients. ML models (logistic regression, random for est, decision tree, support vector machine) and DL models (ResNet 50 and Vgg 19) were used to construct the prediction models. Model performance was evaluated u sing receiver operating characteristic (ROC) curves and the area under the curve (AUC). The cohort included 84 patients in the good prognosis group and 48 patie nts in the poor prognosis group. Univariate and multivariate logistic regression analyses showed that diabetes, RV/LV 1.0, and Qanadli index form independent ri sk factors predicting poor prognosis in patients with APE(P <0.05). A total of 750 texture features were extracted, with 4 key features iden tified through screening. There was a weak positive correlation between texture features and clinical parameters. ROC curves analysis demonstrated AUC values of 0.85 (0.78-0.92), 0.76 (0.67-0.84), and 0.89 (0.83-0.95) for the clinical, text ure feature, and combined models, respectively. In the ML models, the random for est model achieved the highest AUC (0.85), and the support vector machine model achieved the lowest AUC (0.62). And the AUCs for the DL models (ResNet 50 and Vg g 19) were 0.91 (95%CI: 0.90-0.92) and 0.94(95%CI: 0.9 3-0.95), respectively. Vgg 19 model demonstrated exceptional precision (0.93), r ecall (0.76), specificity (0.95) and F1 score (0.84).”
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