查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on Ma chine Learning. According to news reporting originating in Dhaka, Bangladesh, by NewsRx journalists, research stated, “In the continuously developing field of c yber security, user authentication and authorization play a vital role in protec ting personal information and digital assets from unauthorized use. As the field of cyber security expands, traditional user authentication and authorization ap proaches are not enough to prevent unauthorized access to personal information.” Financial support for this research came from Advanced Machine Intelligence Rese arch Lab-AMIR Lab for Supervision and Resources.
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Researchers detail new data in robotic s. According to news originating from Shenyang, People’s Republic of China, by N ewsRx editors, the research stated, “This paper investigates the challenging pro blem of fixed time trajectory tracking for robotic manipulators under the presen ce of unavailable model perturbation, external disturbance and from different in itial states.” Funders for this research include National Natural Science Foundation of China; Applied Basic Research Program of Liaoning Province. The news editors obtained a quote from the research from Northeastern University : “Firstly, a novel fixed-time extended state observer (FESO) is designed to est imate and compensate the lumped disturbance, which is analyzed and proved to be stable in the sense of fixed time bounded stability. Secondly, a new type of fix ed-time prescribed performance control (FPPC) is constructed to guarantee the sy stem convergences to stable state within a predefined time and enhance transient performance. Furthermore, a novel continuous fixed time nonsingular fast termin al sliding mode variable is established, which addresses singularity obstacle in terminal sliding mode. Together with FESO and FPPC, a new fixed-time adaptive n onsingular fast terminal sliding mode controller (FANFTSMC) is developed. Meanwh ile, an adaptive terminal sliding mode reaching law adopted in FANFTSMC promotes the robustness and decreases the chattering phenomenon.”
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 from Edmonton, Ca nada, by NewsRx correspondents, research stated, “Here, we present a workflow fo r analyzing multi-omics data of plasma samples in patients with post-COVID condi tion (PCC). Applicable to various diseases, we outline steps for data preprocess ing and integrating diverse assay datasets.” Our news editors obtained a quote from the research from the University of Alber ta, “Then, we detail statistical analysis to unveil plasma profile changes and i dentify biomarker-clinical variable associations. The last two steps discuss mac hine learning techniques for unsupervised clustering of patients based on their inherent molecular similarities and feature selection to identify predictive bio markers.”
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 new report. According to news originating from Riyadh, Sau di Arabia, by NewsRx editors, the research stated, “Recently, Machine Learning ( ML)-based solutions have been widely adopted to tackle the wide range of securit y challenges that have affected the progress of the Internet of Things (IoT) in various domains. Despite the reported promising results, the ML-based Intrusion Detection System (IDS) proved to be vulnerable to adversarial examples, which po se an increasing threat.” Funders for this research include Research Centre of College of Computer And Inf ormation Sciences, Deanship of Scientific Research, King Saud University.
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on Ma chine Learning. According to news reporting from Chicago, Illinois, by NewsRx jo urnalists, research stated, “Company strategy influences many decisions in freig ht transportation. Behavioral models of company decision-making therefore could benefit from including strategy variables.” Financial support for this research came from U. The news correspondents obtained a quote from the research from the University o f Illinois, “However,strategy is difficult to observe and quantify. Attitudinal surveys of company executives can be used to collect measurements of latent str ategy to use in quantitative models. However, surveys are costly and burdensome. Text mining methods to collect measurements overcome these issues somewhat, but typically require manual intervention and ignore the context of words, which ca n be problematic. This study introduces a new machine learning method to generat e strategy measurement data from existing big text data. The new method, called W2VPCA, combines Natural Language Processing and Principal Components Analysis. W2VPCA produces measurement data that serve as quantitative indicators of latent strategy in behavioral models. W2VPCA is unsupervised, data-driven, and uses in formation on word context. We apply W2VPCA to generate measurements of latent st rategies using readily available, large-scale text data: annual company reports. The empirical measurements are used successfully to associate two latent strate gies, one focusing on distribution and the other on products, with truck fleet a nd distribution center outsourcing decisions. The main empirical outcome is that the W2VPCA measurements outperform Bag-of-Words measurements in a psychometric analysis of latent firm strategies.”
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on ro botics. According to news reporting out of Weifang, People’s Republic of China, by NewsRx editors, research stated, “In response to the navigation requirements of robots in orchard environments, this paper presents a navigation method for o rchard robots based on point cloud maps.” The news reporters obtained a quote from the research from Weifang University: “ First, point cloud maps are processed with pass-through filtering and PCA algori thms to make them suitable for path planning. Next, tree rows within the orchard are clustered and segmented based on map orientations. Then, the least squares method and the $\text{A} ∧{\ast} $ algorithm are combined for global path planning on local maps. Lastly, the TEB l ocal path planning algorithm is employed to ensure that the robot navigates alon g the operation path. Experimental results indicate that the robot can successfu lly navigate orchards at speeds ranging from 0.4 to 1.0 m/s. The average longitu dinal deviation obtained under these conditions is 26.7 cm, with a maximum value not exceeding 46.2 cm.”
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators discuss new findings in Robotics. According to news reporting out of Houston, Texas, by NewsRx editors, research stated, “The recent development of soft fluidic analogs to electrical c omponents aims to reduce the demand for rigid and bulky electromechanical valves and hard electronic controllers within soft robots. This ongoing effort is adva nced in this work by creating sheetbased fluidic diodes constructed from readil y available flexible sheets of polymers and textiles using a layered fabrication approach amenable to manufacturing at scale.” Financial supporters for this research include National Science Foundation Gradu ate Research Fellowship Program, National Aeronautics and Space Administration, National Science Foundation.
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Cardiovascular Disease s and Conditions - Venous Thromboembolism is the subject of a report. According to news reporting from Fuzhou, People’s Republic of China, by NewsRx journalists , research stated, “To identify risk factors that associated with the occurrence of venous thromboembolism (VTE) within 30 days after hysterectomy among gynecol ogical malignant tumor patients, and to explore the value of machine learning (M L) models in VTE occurrence prediction. A total of 1087 patients between January 2019 and January 2022 with gynecological malignant tumors were included in this single-center retrospective study and were randomly divided into the training d ataset (n = 870) and the test dataset (n = 217).” The news correspondents obtained a quote from the research from the Affiliated H ospital of Fujian Medical University, “Univariate logistic regression analysis w as used to identify risk factors that associated with the occurrence of postoper ative VTE in the training dataset. Machine learning models (including decision t ree (DT) model and logistic regression (LR) model) to predict the occurrence of postoperative VTE were constructed and internally validated. The incidence of de veloping 30-day postoperative VTE was 6.0% (65/1087). Age, previou s VTE, length of stay (LOS), tumor stage, operative time, surgical approach, lym phadenectomy (LND), intraoperative blood transfusion and gynecologic Caprini (G- Caprini) score were identified as risk factors for developing postoperative VTE in gynecological malignant tumor patients (p <0.05). The A UCs of LR model and DT model for predicting VTE were 0.722 and 0.950, respective ly.”
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Current study results on Robotics have been publi shed. According to news reporting originating in New Haven, Connecticut, by News Rx journalists, research stated, “Soft robots are predicted to operate well in u nstructured environments due to their resilience to impacts, embodied intelligen ce, and potential ability to adapt to uncertain circumstances. Soft robots are o f further interest for space and extraterrestrial missions, owing to their light weight and compressible construction.” Funders for this research include NASA Small Business Technology Transfer grant, NASA Space Technology Graduate Research Fellowship.
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 out of Xi’an, People’s Republ ic of China, by NewsRx editors, research stated, “Tridacna spp. are valuable arc hives for paleoclimate and paleoweather research due to their distinct daily gro wth patterns and the sensitivity of the daily growth patterns to environment cha nges. However, manually identifying daily growth lines and measuring the daily g rowth increment width (DGIW) of Tridacna shells from Laser Scanning Confocal Mic roscopy (LSCM) images is a tedious task that has become a significant barrier to Tridacna studies.” Our news journalists obtained a quote from the research from Shaanxi Normal Univ ersity, “This paper addresses this challenge by integrating machine learning int o Tridacna research for the first time to automate the calculation of the number of daily growth lines and DGIW of Tridacna shells. Specifically, we propose an unsupervised generative adversarial attention network called TriGAN to automatic ally recognize distinct daily growth lines of Tridacna shells from LSCM images. Utilizing modern Tridacna specimens collected from the South China Sea, our expe rimental results demonstrate that TriGAN can effectively reconstruct the ambiguo us and blurred regions in LSCM images and produce higher quality images of daily growth patterns compared to existing image generation networks. Furthermore, th e daily growth line number and DGIW of Tridacna shells can be counted automatica lly from the images recognized by TriGAN, which are in good agreement with the s tatistical results obtained manually from the original LSCM images (R = 0.7, p<0.01 for the DGIW profile of T. gigas specimen MD1 and R = 0.6, p<0.01 for T. derasa specimen XB10).”
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