查看更多>>摘要:Fresh data on Machine Learning are presented in a new report. According to news reporting originating in Hangzhou, People’s Republic of China, by NewsRx journalists, research stated, “To investigate the value of non-contrast CT (NCCT)-based two-dimensional (2D) radiomics features in predicting haematoma expansion (HE) after spontaneous intracerebral haemorrhage (ICH) and compare its predictive ability with the three-dimensional (3D) signature. Three hundred and seven ICH patients who received baseline NCCT within 6 h of ictus from two stroke centres were analysed retrospectively. 2D and 3D radiomics features were extracted in the manner of one-to-one correspondence.” Financial supporters for this research include National Natural Science Foundation of China (NSFC), Key Research and Development Program of Zhejiang Province. The news reporters obtained a quote from the research from Zhejiang University, “The 2D and 3D models were generated by four different machine-learning algorithms (regularised L1 logistic regression, decision tree, support vector machine and AdaBoost), and the receiver operating characteristic (ROC) curve was used to compare their predictive performance. A robustness analysis was performed according to baseline haematoma volume.Each feature type of 2D and 3D modalities used for subsequent analyses had excellent consistency (mean ICC >0.9). Among the different machine-learning algorithms, pairwise comparison showed no significant difference in both the training (mean area under the ROC curve [AUC] 0.858 versus 0.802, all p>0.05) and validation datasets (mean AUC 0.725 versus 0.678, all p>0.05), and the 10-fold cross-validation evaluation yielded similar results. The AUCs of the 2D and 3D models were comparable either in the binary or tertile volume analysis (all p>0.5). NCCT-derived 2D radiomics features exhibited acceptable and similar performance to the 3D features in predicting HE, and this comparability seemed unaffected by initial haematoma volume.”
查看更多>>摘要:New study results on artificial intelligence have been published. According to news reporting originating from Wroclaw, Poland, by NewsRx correspondents, research stated, “Machine learning models (Support Vector Regression) were applied for predictions of several targets for 18-electron halfHeusler phases: a lattice parameter, a bulk modulus, a band gap, and a lattice thermal conductivity.” Funders for this research include Wroclaw Center For Networking And Supercomputing. The news reporters obtained a quote from the research from Polish Academy of Sciences: “The training subset, which consisted of 47 stable phases, was studied with the use of Density Functional Theory calculations with two Exchange-Correlation Functionals employed (GGA, MBJGGA). The predictors for machine learning models were defined among the basic properties of the elements. The most optimal combinations of predictors for each target were proposed and discussed. Root Mean Squared Errors obtained for the best combinations of predictors for the particular targets are as follows: 0.1 A (lattice parameters), 11-12 Gpa (bulk modulus), 0.22 eV (band gaps, GGA and MBJGGA), and 9-9.5 W/mK (lattice thermal conductivity). The final results of the predictions for a large set of 74 semiconducting half-Heusler compounds were disclosed and compared to the available literature and experimental data.”
查看更多>>摘要:New research on Robotics is the subject of a report. According to news reporting out of Buffalo, New York, by NewsRx editors, research stated, “Product disassembly plays a crucial role in the recycling, remanufacturing, and reuse of end-of-use (EoU) products. However, the current manual disassembly process is inefficient due to the complexity and variation of EoU products.” Funders for this research include National Science Foundation (NSF), Future of Work at the HumanTechnology Frontier (FW-HTF) Program of the National Science Foundation, USA Our news journalists obtained a quote from the research from the State University of New York (SUNY) Buffalo, “While fully automating disassembly is not economically viable given the intricate nature of the task, there is potential in using human-robot collaboration (HRC) to enhance disassembly operations. HRC combines the flexibility and problem-solving abilities of humans with the precise repetition and handling of unsafe tasks by robots. Nevertheless, numerous challenges persist in technology, human workers, and remanufacturing work, which require comprehensive multidisciplinary research to address critical gaps. These challenges have motivated the authors to provide a detailed discussion on the opportunities and obstacles associated with introducing HRC to disassembly.” According to the news editors, the research concluded: “In this regard, the authors have conducted a review of the recent progress in HRC disassembly and present the insights gained from this analysis from three distinct perspectives: technology, workers, and work.”
查看更多>>摘要:Research findings on artificial intelligence are discussed in a new report. According to news reporting originating from Shaoguan, People’s Republic of China, by NewsRx correspondents, research stated, “The hydrological system of thebasin of Lake Urmia is complex, deriving its supply from a network comprising 13 perennial rivers, along withnumerous small springs and direct precipitation onto the lake’s surface.” Funders for this research include Key Improvement Projects of Guangdong Province; Shaoguan Science And Technology Plan Projects; Science And Technology Projects of Education Government in Jiangxi Province. Our news editors obtained a quote from the research from Shaoguan University: “Among these contributors, approximately half of the inflow is attributed to the Zarrineh River and the Simineh River. Remarkably, Lake Urmia lacks a natural outlet, with its water loss occurring solely through evaporation processes. This study employed a comprehensive methodology integrating ground surveys, remote sensing analyses, and meticulous documentation of historical landslides within the basin as primary information sources. Through this investigative approach, we preciselyidentified and geolocated a total of 512 historical landslide occurrences across the Urmia Lake drainage basin, leveraging GPS technology for precision. Thisarticle introduces a suite of hybrid machine learning predictive models, such as support-vector machine (SVM), random forest (RF), decision trees (DT), logistic regression (LR), fuzzy logic (FL), and the technique for order of preference by similarity to the ideal solution (TOPSIS). These models were strategically deployed to assess landslide susceptibility within the region.”
查看更多>>摘要:Data detailed on artificial intelligence have been presented. According to news reporting originating from Hangzhou, People’s Republic of China, by NewsRx correspondents, research stated, “Building height, as an essential measure of urban vertical structure, is key to understanding how urbanization is reshaping inner-city characteristics, particularly in developing countries.” Financial supporters for this research include National Natural Science Foundation of China; Hong Kong Polytechnic University. Our news correspondents obtained a quote from the research from Zhejiang University: “However, estimating building height in urban environments remains challenging. Building height estimation with physical model-based feature approaches and machine learning approaches are limited by a constrained large-scale application capability and the lack of physical significance, respectively. In this study, we proposed a twostep method to estimate building height in spatially heterogeneous urban areas by integrating the merits of machine learning approaches and physical model-based features, together with spatial contextual information. First, we trained a block-level machine learning model on Hangzhou block units to estimate average block-level building height as spatial contextual information. Second, we trained a building-level machine learning model to estimate the final building height of Hangzhou with the estimated spatial contextual information and additional physical model-based features, including radar look angle, building wall orientation, the length of the building, and dielectric constants of the building wall. Our results showed that the proposed method can largely improve the performance of building height estimation, with an overall R2 and RMSE of 0.76 and 6.64 m, respectively.”
查看更多>>摘要:New research on Robotics is the subject of a report. According to news reporting from Rome, Italy, by NewsRx journalists, research stated, “To evaluate the safety and feasibility of the new surgical robot HUGO robotic assisted surgery (RAS) in a series of gynecologic surgical procedures. Between March and October 2022, 138 patients treated at Fondazione Policlinico Universitario A.” The news correspondents obtained a quote from the research from Fondazione Policlinico Universitario Agostino Gemelli IRCCS, “Gemelli IRCCS, Rome, Italy were enrolled in the study. All patients suitable for a minimally-invasive approach were prospectively included and divided into two groups: Group 1 (78 patients) made up of patients operated on for uterine and/or adnexal pathologies, and Group 2 (60 patients) made up of patients treated for pelvic organ prolapse. In Group 1, median docking time (DT) was 5 min and median console time (CT) was 90 min. In two patients (2.6%) redocking was necessary. In two patients (2.6%), the surgeon continued the surgery laparoscopically. Intraoperative complications occurred in two surgeries (2.6%). In Group 2, median DT was 4 min and median CT was 134.5 min. In three patients (5%), redocking was necessary. In all patients, the surgery was successfully completed robotically without intraoperative complications. The present study demonstrates that the new HUGO RAS system for gynecologic surgery is safe with good results in terms of surgical efficacy and perioperative outcomes.” According to the news reporters, the research concluded: “Further studies are needed to investigate its use in other technical and surgical aspects.”
查看更多>>摘要:New research on Machine Translation is the subject of a report. According to news reporting originating from Cairo, Egypt, by NewsRx correspondents, research stated, “Machine translation for low-resource languages poses significant challenges, primarily due to the limited availability of data. In recent years, unsupervised learning has emerged as a promising approach to overcome this issue by aiming to learn translations between languages without depending on parallel data.” Financial support for this research came from Cairo University. Our news editors obtained a quote from the research from Cairo University, “A wide range of methods have been proposed in the literature to address this complex problem. This paper presents an in-depth investigation of semi-supervised neural machine translation specifically focusing on translating Arabic dialects, particularly Egyptian, to Modern Standard Arabic. The study employs two distinct datasets: one parallel dataset containing aligned sentences in both dialects, and a monolingual dataset where the source dialect is not directly connected to the target language in the training data. Three different translation systems are explored in this study. The first is an attention-based sequence-to-sequence model that benefits from the shared vocabulary between the Egyptian dialect and Modern Arabic to learn word embeddings. The second is an unsupervised transformer model that depends solely on monolingual data, without any parallel data.” According to the news editors, the research concluded: “The third system starts with the parallel dataset for an initial supervised learning phase and then incorporates the monolingual data during the training process.”
查看更多>>摘要:Research findings on artificial intelligence are discussed in a new report. According to news reporting originating from Shenzhen, People’s Republic of China, by NewsRx correspondents, research stated, “High-entropy alloys (HEAs) have attracted considerable attention for their exceptional microstructures and properties. Discovering new HEAs with desirable properties is crucial, but traditional design methods are laborious and time-consuming.” Funders for this research include Shenzhen Fundamental Research Program. The news reporters obtained a quote from the research from Shenzhen Research Institute of Shandong University: “Fortunately, the emerging Machine Learning (ML) offers an efficient solution. In this study, composition-microhardness data pairs from various alloy systems were collected and expanded using a Generative Adversarial Network (GAN). These data pairs were converted into empirical parametermicrohardness pairs. Then Active Learning (AL) was employed to screen the Al-Co-Cr-Cu-Fe-Ni system and identify the eXtreme Gradient Boosting (XGBoost) as the optimal ML master model. Millions of data training iterations employing the XGBoost sub-model and accuracy evaluations using the Expected Improvement (EI) algorithm establish the relationship between HEA compositions and microhardness. The proposed sub-model aligns well with experimental data, wherein four Al-rich compositions exhibit ultra-high microhardness (>740 HV, with a maximum of 780.3 HV) and low density (<5.9 g/cm3) in the as-cast bulk state.” According to the news editors, the research concluded: “The hardening increment originates from the precipitation of disordered BCC nanoparticles in the ordered AlCo-rich B2 matrix compared to the dilute B2 AlCo intermetallics. This lightweight, high-performance alloy shows potential for engineering applications as thin films or coatings.”
查看更多>>摘要:Data detailed on artificial intelligence have been presented. According to news originating from the University of British Columbia by NewsRx correspondents, research stated, “Fish mislabeling is a rampant global issue, damaging consumers economic benefits and trust in the fish industry and government authorities, as well as diminishing the efficacy of the sustainability measurement and management of fisheries. Although DNA barcoding as a gold standard method provides accurate identification of biological species of fish, this method is complicated and slow, and requires reagents and solvents.” The news editors obtained a quote from the research from University of British Columbia: “To develop a more rapid, easy-to-use, and environmentally-friendly method for fish species identification, we integrated the non-destructive Raman spectroscopy with chemometrics/machine learning for rapid and simple fish species authentication. Two Raman spectrometers (i.e., a portable Raman spectrometer and a benchtop confocal Raman spectrometer) were used and compared for their performance to identify 11 species of fish (i.e., 4 species of Salmonidae and 7 species of non-Salmonidae). Supervised chemometric/machine learning classification models were constructed based on a hierarchical classification principle to solve this 11-class identification problem. Both Raman spectrometers were able to differentiate Salmonidae from non-Salmonidae fish with close to 100% accuracy (i.e., first-hierarchical level). To further identify the fish to species level, the portable Raman spectrometer provided better accuracy (i.e., 93% and 93% accuracy for the Salmonidae group and non-Salmonidae group of fish identification, respectively) compared to the benchtop Raman spectrometer (i.e., 90% and 84% accuracy for the Salmonidae group and nonSalmonidae group of fish identification, respectively). The overall analytical time from sample to results can be completed within 5 min, much faster compared to the gold standard method.”
查看更多>>摘要:New research on Artificial Intelligence is the subject of a report. According to news reporting from Guilin, People’s Republic of China, by NewsRx journalists, research stated, “The prediction of patient disease risk via computed tomography (CT) images and artificial intelligence techniques shows great potential. However, training a robust artificial intelligence model typically requires large-scale data support.” The news correspondents obtained a quote from the research from the Guilin University of Aerospace Technology, “In practice, the collection of medical data faces obstacles related to privacy protection. Therefore, the present study aims to establish a robust federated learning model to overcome the data island problem and identify high-risk patients with postoperative gastric cancer recurrence in a multicentre, cross-institution setting, thereby enabling robust treatment with significant value. In the present study, we collect data from four independent medical institutions for experimentation. The robust federated learning model algorithm yields area under the receiver operating characteristic curve (AUC) values of 0.710, 0.798, 0.809, and 0.869 across four data centres.” According to the news reporters, the research concluded: “Additionally, the effectiveness of the algorithm is evaluated, and both adaptive and common features are identified through analysis.”