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 originating from Cagli ari, Italy, by NewsRx correspondents, research stated, "Carotid atherosclerosis plays a substantial role in cardiovascular morbidity and mortality. Given the mu ltifaceted impact of this disease, there has been increasing interest in harness ing artificial intelligence (AI) and radiomics as complementary tools for the qu antitative analysis of medical imaging data." Our news editors obtained a quote from the research from the University of Cagli ari, "This integrated approach holds promise not only in refining medical imagin g data analysis but also in optimizing the utilization of radiologists' expertis e. By automating time consuming tasks, AI allows radiologists to focus on more p ertinent responsibilities. Simultaneously, the capacity of AI in radiomics to ex tract nuanced patterns from raw data enhances the exploration of carotid atheros clerosis, advancing efforts in terms of (1) early detection and diagnosis, (2) r isk stratification and predictive modeling, (3) improving workflow efficiency, a nd (4) contributing to advancements in research. This review provides an overvie w of general concepts related to radiomics and AI, along with their application in the field of carotid vulnerable plaque."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-New research on Kidney Diseases and Conditions - Chronic Kidney Disease is the subject of a report. According to news reporting o riginating in Beijing, People's Republic of China, by NewsRx journalists,resear ch stated, "Previous studies have identified several genetic and environmental r isk factors for chronic kidney disease (CKD). However, little is known about the relationship between serum metals and CKD risk." The news reporters obtained a quote from the research from Air Force Medical Cen ter, "We investigated associations between serum metals levels and CKD risk amon g 100 medical examiners and 443 CKD patients in the medical center of the First Hospital Affiliated to China Medical University. Serum metal concentrations were measured using inductively coupled plasma mass spectrometry (ICP-MS). We analyz ed factors influencing CKD, including abnormalities in Creatine and Cystatin C, using univariate and multiple analysis such as Lasso and Logistic regression. Me tal levels among CKD patients at different stages were also explored. The study utilized machine learning and Bayesian Kernel Machine Regression (BKMR) to asses s associations and predict CKD risk based on serum metals. A chained mediation m odel was applied to investigate how interventions with different heavy metals in fluence renal function indicators (creatinine and cystatin C) and their impact o n diagnosing and treating renal impairment. Serum potassium (K), sodium (Na), an d calcium (Ca) showed positive trends with CKD, while selenium (Se) and molybden um (Mo) showed negative trends. Metal mixtures had a significant negative effect on CKD when concentrations were all from 30 to 45 percentiles compared to the m edian, but the opposite was observed for the 55 to 60 percentiles. For example, a change in serum K concentration from the 25 to the 75 percentile was associate d with a significant increase in CKD risk of 5.15(1.77,8.53), 13.62(8.91,18.33) and 31.81(14.03,49.58) when other metals were fixed at the 25, 50 and 75 percent iles, respectively. Cumulative metal exposures, especially double-exposure to se rum K and Se may impact CKD risk. Machine learning methods validated the externa l relevance of the metal factors."
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 - Intelligent Systems are discussed in a new report. According to news reporting from Shanghai, People's Republic of China, by NewsRx journalists, research stat ed, "Red tide data are typical multivariate time series (MTS) and complete data help analyze red tide more conveniently. However, missing values due to artifici al or accidental events hinder further analysis of red tide phenomenon." The news correspondents obtained a quote from the research from Shanghai Univers ity, "Generative adversarial network (GAN) is effective in capturing distributio n of MTS while the imputation performance is far from satisfactory, especially i n conditions of high missing rate. One of the remaining open challenges is that common GAN-based imputation methods usually lack the ability to excavate implici t correlations between different attributions and downstream tasks, from which a dvanced latent information about missing values can be mined to improve imputati on performance. To deal with the problem, a novel multi-task learning-based gene rative adversarial imputation network (MTGAIN) is proposed by introducing the pr ediction task into GAN to unearth more detailed information about missing values to better model distribution of red tide MTS. Furthermore, the homoscedastic un certainty of multiple tasks is exploited to balance the weights of losses betwee n generation and prediction tasks."
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 - Rectal Canc er is the subject of a report. According to news reporting from Chengdu, People' s Republic of China, by NewsRx journalists, research stated, "To evaluate the pe rformance of machine learning models in predicting treatment response to neoadju vant chemoradiotherapy (nCRT) in rectal cancer using computed tomography (CT) an d magnetic resonance imaging (MRI). We searched PubMed, Embase, Cochrane Library , and Web of Science for studies published before January 2023." The news correspondents obtained a quote from the research, "The Quality Assessm ent of Diagnostic Accuracy Studies 2 (QUADAS-2) was used to assess the methodolo gical quality of the included studies, random-effects models were used to calcul ate sensitivity and specificity, I2 values were used for heterogeneity measureme nts, and subgroup analyses were carried out to detect potential sources of heter ogeneity. A total of 1690 patients from 24 studies were included. The meta-analy sis calculated a pooled area under the curve (AUC) of 0.92 (95%CI-0 .89-0.94), pooled sensitivity of 0.81 (95%CI-0.73-0.88), and pooled specificity of 0.88 (95%CI-0.82-0.92). We investigated 4 studies t hat mainly contributed to heterogeneity. After performing meta-analysis again ex cluding these 4 studies, the heterogeneity was significantly reduced. In subgrou p analysis, the pooled AUC of the deep learning model was 0.95 and was 0.88 for the traditional statistical model; the pooled AUC of studies that used diffusion -weighted imaging (DWI) was 0.90, and was 0.92 in studies that did not use DWI; the pooled AUC of studies conducted in China was 0.94, and was 0.83 in studies c onducted in other countries. Machine learning has promising potential in predict ing tumor response to nCRT in patients with locally advanced rectal cancer. Toge ther with clinical information, machine-learning based models may bring us close r toward precision medicine. Compared to traditional machine learning models, de ep learning-based studies are able to obtain higher AUC, although they are less predominant and more heterogeneous."
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 in Changsha, Peop le's Republic of China, by NewsRx journalists, research stated, "Identifying the key influencing factors in soil available cadmium (Cd) is crucial for preventin g the Cd accumulation in the food chain. However, current experimental methods a nd traditional prediction models for assessing available Cd are time-consuming a nd ineffective." The news reporters obtained a quote from the research from Central South Univers ity, "In this study, machine learning (ML) models were developed to investigate the intricate interactions among soil properties, climate features, and availabl e Cd, aiming to identify the key influencing factors. The optimal model was obta ined through a combination of stratified sampling, Bayesian optimization, and 10 -fold cross-validation. It was further explained through the utilization of perm utation feature importance, 2D partial dependence plot, and 3D interaction plot. The findings revealed that pH, surface pressure, sensible heat net flux and org anic matter content significantly influenced the Cd accumulation in the soil. By utilizing historical soil surveys and climate change data from China, this stud y predicted the spatial distribution trend of available Cd in the Chinese region , highlighting the primary areas with heightened Cd activity. These areas were p rimarily located in the eastern, southern, central, and northeastern China. This study introduces a novel methodology for comprehending the process of available Cd accumulation in soil."
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 from Pavia, Italy, by NewsRx journalists, research stated, "Computational approaches can provide highl y detailed insight into the molecular recognition processes that underlie drug b inding, the assembly of protein complexes, and the regulation of biological func tional processes. Classical simulation methods can bridge a wide range of length - and time-scales typically involved in such processes." The news correspondents obtained a quote from the research from the University o f Pavia, "Lately, automated learning and artificial intelligence methods have sh own the potential to expand the reach of physics-based approaches, ushering in t he possibility to model and even design complex protein architectures. The syner gy between atomistic simulations and AI methods is an emerging frontier with a h uge potential for advances in structural biology." According to the news reporters, the research concluded: "Herein, we explore var ious examples and frameworks for these approaches, providing select instances an d applications that illustrate their impact on fundamental biomolecular problems ."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Research findings on robotics are disc ussed in a new report. According to news originating from Tokyo, Japan, by NewsR x correspondents, research stated, "In this paper, we describe a sheet-shaped th rowable transforming robot." Financial supporters for this research include Jsps Kakenhi.The news reporters obtained a quote from the research from University of Electro -Communications: "Sheet-type robots can change their shape to perform tasks acco rding to the situation. Therefore, they are expected to be useful in places with many restrictions, such as disaster sites. However, most of them can only move slowly on the ground. Therefore, in order to actually deliver the robot to the d isaster site, it must be carried manually. To solve this problem, we are develop ing a sheet-shaped robot that can be thrown from the sky."
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 originating from Shanghai, People's Rep ublic of China, by NewsRx correspondents, research stated, "The intricate develo pment of liquid-crystal lubricants necessitates the timely and accurate predicti on of their tribological performance in different environments and an assessment of the importance of relevant parameters. In this study, a classification model using Gaussian noise extreme gradient boosting (GNBoost) to predict tribologica l performance is proposed." Our news journalists obtained a quote from the research from the Chinese Academy of Sciences, "Three additives, polysorbate-85, polysorbate-80, and graphene oxi de, were selected to fabricate liquidcrystal lubricants. The coefficients of fr iction of these lubricants were tested in the rotational mode using a universal mechanical tester. A model was designed to predict the coefficient of friction t hrough data augmentation of the initial data. The model parameters were optimize d using particle swarm optimization techniques." According to the news editors, the research concluded: "This study provides an e ffective example for lubricant performance evaluation and formulation optimizati on."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Data detailed on Machine Learning have been prese nted. According to news reporting originating in Wuhan, People's Republic of Chi na, by NewsRx journalists, research stated, "The performance evaluation of learn ing algorithms is essential in underwater acoustic target recognition. Underwate r acoustic data typically show temporal structures." The news reporters obtained a quote from the research from the Naval University of Engineering, "However, these structures are frequently ignored when evaluatin g the performance of learning algorithms, resulting in overestimating predictive accuracy. The similarity analysis of underwater acoustic samples indicates clus ter structures in the feature space, which follow a manifold distribution over t ime. A uniform block cross-validation method and a clustering block crossvalidat ion method are proposed to evaluate the performance of learning algorithms. The effect of block size is investigated using the simulated and the real data. The results indicate that the proposed clustering block crossvalidation method is su itable for evaluating algorithms when interpolation is prediction objective only . The uniform block cross-validation is suitable for evaluating algorithms ‘ int erpolation and extrapolation abilities. Moreover, the proposed two methods are s uperior to the random cross-validation method."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Data detailed on robotics have been pr esented. According to news reporting originating from Cottbus, Germany, by NewsR x correspondents, research stated, "The emerging integration of Brain- Computer I nterfaces (BCIs) in human-robot collaboration holds promise for dynamic adaptive interaction. The use of electroencephalogram (EEG)-measured error-related poten tials (ErrPs) for online error detection in assistive devices offers a practical method for improving the reliability of such devices." Our news correspondents obtained a quote from the research from Brandenburg Univ ersity of Technology: "However, continuous online error detection faces challeng es such as developing efficient and lightweight classification techniques for qu ick predictions, reducing false alarms from artifacts, and dealing with the non- stationarity of EEG signals. Further research is essential to address the comple xities of continuous classification in online sessions. With this study, we demo nstrated a comprehensive approach for continuous online EEG-based machine error detection, which emerged as the winner of a competition at the 32nd Internationa l Joint Conference on Artificial Intelligence. The competition consisted of two stages: an offline stage for model development using pre-recorded, labeled EEG d ata, and an online stage 3 months after the offline stage, where these models we re tested live on continuously streamed EEG data to detect errors in orthosis mo vements in real time. Our approach incorporates two temporalderivative features with an effect size-based feature selection technique for model training, toget her with a lightweight noise filtering method for online sessions without recali bration of the model. The model trained in the offline stage not only resulted i n a high average cross-validation accuracy of 89.9% across all par ticipants, but also demonstrated remarkable performance during the online sessio n 3 months after the initial data collection without further calibration, mainta ining a low overall false alarm rate of 1.7% and swift response ca pabilities. Our research makes two significant contributions to the field. First ly, it demonstrates the feasibility of integrating two temporal derivative featu res with an effect size-based feature selection strategy, particularly in online EEG-based BCIs."
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