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    Huaqiao University Reports Findings in Machine Learning (ResDNet: A model for rapid prediction of antioxidant activity in gentian root using FT-IR spectroscopy)

    39-39页
    查看更多>>摘要:New research on Machine Learning is the subject of a report. According to news reporting out of Quanzhou, People’s Republic of China, by NewsRx editors, research stated, “Gentian, an herb resource known for its antioxidant properties, has garnered significant attention. However, existing methods are time-consuming and destructive for assessing the antioxidant activity in gentian root samples.” Our news journalists obtained a quote from the research from Huaqiao University, “In this study, we propose a method for swiftly predicting the antioxidant activity of gentian root using FT-IR spectroscopy combined with chemometrics. We employed machine learning and deep learning models to establish the relationship between FT-IR spectra and DPPH free radical scavenging activity. The results of model fitting reveal that the deep learning model outperforms the machine learning model. The model’s performance was enhanced by incorporating the Double-Net and residual connection strategy. The enhanced model, named ResD-Net, excels in feature extraction and also avoids gradient vanishing. The ResD-Net model achieves an R of 0.933, an RMSE of 0.02, and an RPD of 3.856.”

    New Machine Translation Study Findings Recently Were Reported by Researchers at University of Science and Technology China (Datastore Distillation for Nearest Neighbor Machine Translation)

    40-40页
    查看更多>>摘要:Data detailed on Machine Translation have been presented. According to news reporting from Anhui, People’s Republic of China, by NewsRx journalists, research stated, “Nearest neighbor machine translation (i.e., kNN-MT) is a promising approach to enhance translation quality by equipping pre-trained neural machine translation (NMT) models with the nearest neighbor retrieval. Despite its great success, kNN-MT typically requires ample space to store its token-level datastore, causing kNN-MT to be less practical in edge devices or online scenarios.” Financial support for this research came from National Natural Science Foundation of China (NSFC). The news correspondents obtained a quote from the research from the University of Science and Technology China, “In this paper, inspired by the concept of knowledge distillation, we provide a new perspective to ease the storage overhead by datastore distillation, which is formalized as a constrained optimization problem. We further design a novel model-agnostic iterative nearest neighbor merging method for the datastore distillation problem to obtain an effective and efficient solution. Experiments on three benchmark datasets indicate that our approach not only reduces the volume of the datastore by up to 50% without significant performance degradation, but also outperforms other baselines by a large margin at the same compression rate.”

    Erciyes University Reports Findings in Escherichia coli O157:H7 (Machine learning models for prediction of Escherichia coli O157:H7 growth in raw ground beef at different storage temperatures)

    41-41页
    查看更多>>摘要:New research on Foodborne Diseases and Conditions - Escherichia coli O157:H7 is the subject of a report. According to news reporting from Kayseri, Turkey, by NewsRx journalists, research stated, “Shiga toxin-producing Escherichia coli (STEC) can be life-threatening and lead to major outbreaks. The prevention of STEC-related infections can be provided by control measures at all stages of the food chain.” The news correspondents obtained a quote from the research from Erciyes University, “The growth performance of E. coli O157:H7 at different temperatures in raw ground beef spiked with cocktail inoculum was investigated using machine learning (ML) models to address this problem. After spiking, ground beef samples were stored at 4, 10, 20, 30 and 37 ℃. Repeated E. coli O157 enumeration was performed at 0-96 h with 21 times repeated counting. The obtained microbiological data were evaluated with ML methods (Artificial Neural Network (ANN), Random Forest (RF), Support Vector Regression (SVR), and Multiple Linear Regression (MLR)) and statistically compared for valid prediction. The coefficient of determination ® and mean squared error (MSE) are two essential criteria used to evaluate the model performance regarding the comparison between the observed value and the prediction made by the model. RF model showed superior performance with 0.98 R and 0.08 MSE values for predicting the growth performance of E. coli O157 at different temperatures. MLR model predictions were obtained further from the observed values with 0.66 R and 2.7 MSE values.”

    New Findings from Chukyo University in the Area of Robotics Published (Investigating Emotional Impressions in Robots Using Clothing Colors)

    42-42页
    查看更多>>摘要:Investigators discuss new findings in robotics. According to news reporting out of Aichi, Japan, by NewsRx editors, research stated, “Expressing emotions is essential for ensuring smooth communication between people.” Our news correspondents obtained a quote from the research from Chukyo University: “In the context of human-robot symbiosis, robots are also required to express emotions. Although one method of robot emotion expression involves using LEDs or other forms of light to display colors, considering the possibility of expressing emotions through clothing colors is also necessary. In this study, we developed a simple robot called the “Tilting Robot,” which only performs simple tilting motions to investigate whether changes in the robot’s clothing color would affect the expressed emotions. In the experiment, participants were divided into two groups: motion and posture groups. The motion group was shown videos of the robot’s motion whereas the posture group was shown still images of the robot’s posture. The results showed that the red clothing in the posture group significantly expressed anger, whereas the blue clothing in the motion group significantly expressed sadness.”

    Researchers from Shanghai Jiao Tong University Describe Findings in Robotics (Toolpath Generation for Robotic Flank Milling Via Smoothness and Stiffness Optimization)

    42-43页
    查看更多>>摘要:A new study on Robotics is now available. According to news originating from Shanghai, People’s Republic of China, by NewsRx correspondents, research stated, “Robotic flank milling has outstanding advantages in machining large-scale slender surfaces. Currently, the paths for this process are mainly generated by optimizing redundant robot degrees of freedom (DoFs) on the basis of conventional 5-axis flank milling paths.” Financial support for this research came from National Natural Science Foundation of China (NSFC). Our news journalists obtained a quote from the research from Shanghai Jiao Tong University, “This twostep framework, however, does not enable optimal robot kinematic and dynamical performance compared to the direct generation of 6-DoF robot paths, limiting the machining efficiency and effectiveness. This paper presents an optimization method to directly generate a toolpath with six DoFs for robotic flank milling. Firstly, the kinematic model of the milling system and the representation of the 6-DoF toolpath are established. Then, the standard geometric error for flank milling that conforms to the geometric specification is defined, and an efficient algorithm based on conformal geometric algebra is proposed to solve it. On this basis, the toolpath optimization model with toolpath smoothness and robot stiffness as objective functions is established. A sequential quadratic programming algorithm is proposed to solve this highly non-linear problem based on the lexicographic order of arrays. The simulations and experiments demonstrate that the proposed method has better efficiency, robustness, and effectiveness compared with the existing methods.”

    University Medical Center Ljubljana Reports Findings in Broca Aphasia (Integrating EEG and Machine Learning to Analyze Brain Changes during the Rehabilitation of Broca's Aphasia)

    43-44页
    查看更多>>摘要:New research on Speech Language and Learning Diseases and Conditions - Broca Aphasia is the subject of a report. According to news originating from Ljubljana, Slovenia, by NewsRx correspondents, research stated, “The fusion of electroencephalography (EEG) with machine learning is transforming rehabilitation. Our study introduces a neural network model proficient in distinguishing preand post-rehabilitation states in patients with Broca’s aphasia, based on brain connectivity metrics derived from EEG recordings during verbal and spatial working memory tasks.” Financial support for this research came from Javna Agencija za Raziskovalno Dejavnost RS.

    New Intelligent Systems Study Results Reported from Nanchang Hangkong University (A Novel Ce-pt-mabac Method for T-spherical Uncertain Linguistic Multiple Attribute Group Decision-making)

    44-45页
    查看更多>>摘要:A new study on Machine Learning - Intelligent Systems is now available. According to news reporting out of Nanchang, People’s Republic of China, by NewsRx editors, research stated, “A T-spherical uncertain linguistic set (TSULS) is not only an expanded form of the T-spherical fuzzy set and the uncertain linguistic set but can also integrate the quantitative judging ideas and qualitative assessing information of decision-makers. For the description of complex and uncertain assessment data, TSULS is a powerful tool for the precise description and reliable processing of information data.” Funders for this research include Humanities and Social Sciences Foundation of Ministry of Education of the People’s Republic of China, Office of Research, Innovation, and Commercialization (ORIC) of Riphah International University Lahore.

    Investigators from Harbin Institute of Technology Shenzhen Zero in on Robotics (Torque-bounded Admittance Control With Implicit Euler Realization of Set-valued Operators)

    45-46页
    查看更多>>摘要:Fresh data on Robotics are presented in a new report. According to news reporting originating in Shenzhen, People’s Republic of China, by NewsRx journalists, research stated, “When a robot collides with environments of unknown stiffness, the resultant torque saturation can cause the conventional admittance control to exhibit unsafe behaviors such as large resilience force, oscillation, and snapping back. To address this challenge, this article proposes a novel torque-bounded admittance control algorithm that can quickly stabilize the robot to maintain a safe and compliant contact force, while also guaranteeing position tracking accuracy in free space.” Financial support for this research came from National Key Research and Development Program of China.

    Researchers from University of Rey Juan Carlos Describe Findings in Machine Learning (Effectiveness of Tutoring At School: a Machine Learning Evaluation)

    46-47页
    查看更多>>摘要:Research findings on Machine Learning are discussed in a new report. According to news reporting originating in Madrid, Spain, by NewsRx journalists, research stated, “Tutoring programs are effective in reducing school failures among at-risk students. However, there is still room for improvement in maximising the social returns they provide on investments.Many factors and components can affect student engagement in a program and academic success.” The news reporters obtained a quote from the research from the University of Rey Juan Carlos, “This complexity presents a challenge for Public Administrations to use their budgets as efficiently as possible. Our research focuses on providing public administration with advanced decision-making tools.First, we analyse a database with information on 2066 students of the Programa para la Mejora de ‘ Exito Educativo (Programme for the Improvement of Academic Success) of the Junta de Comunidades de Castilla y Le ‘ on in Spain, in 2018-2019, the academic year previous to the pandemic. This program is designed to help schools with students at risk of failure in Spanish, literature, mathematics, and English. We developed a machine learning model (ML) based on Kohonen self-organising maps (SOMs), which are a type of unsupervised (ANN), to group students based on their characteristics, the type of tutoring program in which they were enrolled, and their results in both the completion of the program and the 4th year of Compulsory Secondary Education (ESO).Second, we evaluated the results of tutoring programs and identified and explained how different factors and components affect student engagement and academic success.”

    Klaipeda University Hospital Reports Findings in Surgical Technology (Robotic-assisted radical prostatectomy: a multicenter experience with the Senhance Surgical System)

    47-48页
    查看更多>>摘要:New research on Surgery - Surgical Technology is the subject of a report. According to news reporting out of Klaipeda, Lithuania, by NewsRx editors, research stated, “Robotic-assisted surgery for radical prostatectomy is becoming a standard treatment, and respective implementations are expanding. The Senhance Surgical System is a robotic system with existing but limited data on radical prostatectomy, including a lack of multicenter study experiences.” Our news journalists obtained a quote from the research from Klaipeda University Hospital, “The TRUST study aims to fill this gap and explores observations for radical prostatectomy with the Senhance Surgical System. Between August 2019 and November 2022, 375 patients met inclusion criteria from two European sites. Patients’ surgical procedure times, data on conversion, malfunction, adverse events, and pain scores were registered and evaluated. Outcomes were calculated for both sides, combined as a total and compared between the initial (1st-150th case) and later (>150th case) period. The median operating time was 190 min (IQR: 167.5-215.0) and the median docking time was 3 min (IQR: 2.0-5.0). Eighteen cases (4.8%) were converted to standard laparoscopy and two (0.5%) to open. Two perioperative (0.5%) and eleven postoperative adverse events (2.9%) occurred, mostly (83.3%) categorized as mild. Pain scores were reduced from an average of 3.4 (± 1.4) on the postoperative day to 0.9 (± 0.7) at discharge. Compared to our previous data and based on a comparison between our initial and later period, operating time seems to plateau. However, docking time, complication, and conversion rates were successfully reduced.”