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    Sichuan University Reports Findings in Thyroid Cancer (Contrastenhanced ultraso und image sequences based on radiomics analysis for diagnosis of metastatic cerv ical lymph nodes from thyroid cancer)

    48-49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 Oncology - Thyroid Can cer is the subject of a report. According to news reporting out of Chengdu, Peop le's Republic of China, by NewsRx editors, research stated, "Thyroid cancer (TC) prone to cervical lymph node (CLN) metastasis both before and after surgery. Ul trasonography (US) is the first-line imaging method for evaluating the thyroid g land and CLNs." Our news journalists obtained a quote from the research from Sichuan University, "However, this assessment relies mainly on the subjective judgment of the sonog rapher and is very much dependent on the sonographer's experience. This prospect ive study was designed to construct a machine learning model based on contrast-e nhanced ultrasound (CEUS) videos of CLNs to predict the risk of CLN metastasis i n patients with TC. Patients who were proposed for surgical treatment due to TC from August 2019 to May 2020 were prospectively included. All patients underwent US of CLNs suspected of metastasis, and a 2- minute imaging video was recorded. After target tracking, feature extraction, and feature selection through the lym ph node imaging video, three machine learning models, namely, support vector mac hine, linear discriminant analysis (LDA), and decision tree (DT), were construct ed, and the sensitivity, specificity, and accuracy of each model for diagnosing lymph nodes were calculated by leave-one-out cross-validation (LOOCV). A total o f 75 lymph nodes were included in the study, with 42 benign cases and 33 maligna nt cases. Among the machine learning models constructed, the support vector mach ine had the best diagnostic efficacy, with a sensitivity of 93.0%, a specificity of 93.8%, and an accuracy of 93.3%."
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    Study Findings on Machine Learning Reported by a Researcher at South China Unive rsity of Technology (Application and Analysis of LSTM and GRU Models for Cryptoc urrency Return Prediction)

    49-50页
    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 artificial intelligence. According to news originating from Guangzhou, People's Republic of China, by NewsRx correspondents, research stated, "Since the beginni ng of the 2010s, cryptocurrency has been gaining abundant attention and has beco me a worthy considered asset in individual investment portfolio arrangements." Our news journalists obtained a quote from the research from South China Univers ity of Technology: "In this way, research on its return prediction is needed to provide guidance for investors to realize their maximum interests. This research delves into the use of prominent machine learning methods for forecasting crypt ocurrency returns, with a particular emphasis on two advanced models: Long Short -Term Memory (LSTM) and Gated Recurrent Unit (GRU). By scrutinizing a range of a cademic studies, we identify the strengths and weaknesses of these models in ret urn prediction and offer a comparative analysis. Our findings reveal that the GR U model excels by achieving lower values in both Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE), highlighting its superior predictive accuracy. Meanwhile, LSTM presents plausible recAll rates, precision, accuracy, and lower cross-entropy losses."
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    Reports Outline Robotics Study Findings from Tianjin University (Extended State Observer-based Trajectory Tracking Control of a Wheeled Mobile Robot With One Un powered Trailer)

    50-51页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Current study results on Robotics have been published. According to news originating from Tianjin, People's Republic o f China, by NewsRx correspondents, research stated, "In this paper, trajectory t racking control is investigated for a wheeled mobile robot with one unpowered tr ailer using an extended state observer (ESO). The unpowered trailer is added to improve load capacity, which results in a large impact on trajectory tracking as a slow-varying and large disturbance." Financial support for this research came from National Natural Science Foundatio n of China (NSFC). Our news journalists obtained a quote from the research from Tianjin University, "A backstepping controller is proposed to generate desired velocities in an out er loop of a double closed-loop structure. The ESO is employed in an inner loop to estimate the slow-varying and large disturbance from the unpowered trailer. A n integral sliding mode controller is also designed in the inner loop to track the desired velocities from the outer loop. Stability analysis for the ESO, the b ackstepping controller and the integral sliding mode controller is conducted via Lyapunov methods."
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    Findings from Department of Automatics Advance Knowledge in Robotics (Improving Ergonomics of Collaborative Robot Workcells Using Passive Reconfigurable Fixture s)

    51-51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 the Department of Automatics by NewsRx co rrespondents, research stated, "In contemporary manufacturing settings, adaptabi lity and human-robot cooperation are crucial to addressing the chAllenges of hig h-mix, low-volume production." Funders for this research include Slovenian Research Agency; European Union Hori zon 2020 Research And Innovation Action Reconcycle; Digitop Project Funded By Mi nistry of Higher Education; Science And Innovation of Slovenia, Slovenian Resear ch And Innovation Agency, And Eu-nextgenerationeu; Slovenian Research Agency Thr ough The Program Group "automation, Robotics And Biocybernetics". Our news editors obtained a quote from the research from Department of Automatic s: "This paper presents a novel methodology to improve workcell ergonomics by op timAlly placing reconfigurable fixtures and other workcell elements. Based on th e personalized upper body models of human workers, we define a comfortable works pace for each arm. Through the development of comprehensive optimization problem s, we compute workcell configurations to align with individual ergonomic needs w ithout compromising production goals. We validate our approach through case stud ies in the automotive sector, including the assembly of light housings and car s tarters. Results from these studies, based on objective and subjective metrics, affirm the potential of reconfigurable workcell elements to improve ergonomic co nditions for workers, thereby contributing to safer and more productive manufact uring environments."
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    Findings from Beijing Jiaotong University in the Area of Machine Learning Descri bed (Machine Learning Based Laser Homogenization Method)

    52-53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 Machine Learning is now available. According to news reporting originating from Beijing, People's Repub lic of China, by NewsRx correspondents, research stated, "Laser is widely used i n various fields such as laser processing, optical imaging, and optical trapping due to its high monochromaticity, directionality, and high energy density. Howe ver, the beam generated by the laser is a Gaussian beam with non-uniform distrib ution of optical energy, and this non-uniform distribution affects the interacti on between the laser and the matter." Financial support for this research came from National Natural Science Foundatio n of China (NSFC). Our news editors obtained a quote from the research from Beijing Jiaotong Univer sity, "Therefore, it is necessary to reshape the Gaussian beam into homogenized light spots with uniform distribution of optical energy. Laser beam homogenizati on method aims to change the spatial distribution of the Gaussian beam, precisel y controlling the shape and intensity of the laser beam to achieve homogenized l ight spots. However, the existing laser beam homogenization methods encounter so me problems such as complicated component preparation and poor flexibility. They also fail to address experimental errors caused by stray light and zero-order l ight interference, leading to discrepancies between the experimental results and the expected results. These limitations seriously restrict the widespread appli cation of laser technology in various fields. A laser homogenization method base d on machine learning is proposed for spatial light modulator (SLM) laser homoge nization in this work. The preliminary approach to laser homogenization is to ge nerate a phase hologram by using the Gerchberg-Saxton (G-S) algorithm and modula te the incident light beam into homogenized light spots by using an SLM. However , the inherent homogenization error of the SLM prevents laser homogenization fro m improving uniformity. The machine learning method is proposed as a means of co mpensating for homogenization errors, thereby improving the uniformity of the li ght spot. The corresponding supervised learning regression task on the experimen tal dataset establishes mapping relationships between the homogenization target images and the experimental detection images. The results of homogenization erro r compensation are validated through experiments. Compared with the traditional SLM laser homogenization methods, the proposed method reduces the non-uniformity of the light spot by 13%. The laser homogenization method based on machine learning is an efficient way to achieve laser beam homogenization. The proposed laser beam homogenization method can serve as a reference for machine l earning-based method. This method possesses significant technical value for lase r applications such as laser processing, optical imaging, and optical manipulati on."
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    Findings from Anhui University Yields New Data on Computational Intelligence (A Deep Reinforcement Learning-based Adaptive Large Neighborhood Search for Capacit ated Electric Vehicle Routing Problems)

    53-54页
    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 Machine Learning - Computational Intelligence. According to news reporting out of Hefei , People's Republic of China, by NewsRx editors, research stated, "The Capacitat ed Electric Vehicle Routing Problem (CEVRP) poses a novel chAllenge within the Field of vehicle routing optimization, as it requires consideration of both custo mer service requirements and electric vehicle recharging schedules. In addressin g the CEVRP, Adaptive Large Neighborhood Search (ALNS) has garnered widespread a cclaim due to its remarkable adaptability and versatility." Funders for this research include National Natural Science Foundation of China ( NSFC), Natural Science Foundation of Anhui Province. Our news journalists obtained a quote from the research from Anhui University, " However, the original ALNS, using a weight-based scoring method, relies solely o n the past performances of operators to determine their weights, thereby failing to capture crucial information about the ongoing search process. Moreover, it o ften employs a fixed single charging strategy for the CEVRP, neglecting the pote ntial impact of alternative charging strategies on solution improvement. Therefo re, this study treats the selection of operators as a Markov Decision Process and introduces a novel approach based on Deep Reinforcement Learning (DRL) for ope rator selection. This approach enables adaptive selection of both destroy and re pair operators, alongside charging strategies, based on the current state of the search process. More specificAlly, a state extraction method is devised to extr act features not only from the problem itself but also from the solutions genera ted during the iterative process. AdditionAlly, a novel reward function is desig ned to guide the DRL network in selecting an appropriate operator portfolio for the CEVRP. Experimental results demonstrate that the proposed algorithm excels i n instances with fewer than 100 customers, achieving the best values in 7 out of 8 test instances."
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    Tsinghua University Reports Findings in Sepsis (Prediction of sepsis within 24 h ours at the triage stage in emergency departments using machine learning)

    54-55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 Blood Diseases and Con ditions - Sepsis is the subject of a report. According to news reporting origina ting from Beijing, People's Republic of China, by NewsRx correspondents, researc h stated, "Sepsis is one of the main causes of mortality in intensive care units (ICUs). Early prediction is critical for reducing injury." Our news editors obtained a quote from the research from Tsinghua University, "A s approximately 36 % of sepsis occur within 24 h after emergency de partment (ED) admission in Medical Information Mart for Intensive Care (MIMIC-IV ), a prediction system for the ED triage stage would be helpful. Previous method s such as the quick Sequential Organ Failure Assessment (qSOFA) are more suitabl e for screening than for prediction in the ED, and we aimed to find a light-weig ht, convenient prediction method through machine learning. We accessed the MIMIC -IV for sepsis patient data in the EDs. Our dataset comprised demographic inform ation, vital signs, and synthetic features. Extreme Gradient Boosting (XGBoost) was used to predict the risk of developing sepsis within 24 h after ED admission . AdditionAlly, SHapley Additive exPlanations (SHAP) was employed to provide a c omprehensive interpretation of the model's results. Ten percent of the patients were randomly selected as the testing set, while the remaining patients were use d for training with 10-fold cross-validation. For 10-fold cross-validation on 14 ,957 samples, we reached an accuracy of 84.1%±0.3% and an area under the receiver operating characteristic (ROC) curve of 0.92±0.02. The model achieved similar performance on the testing set of 1,662 patients. SHA P values showed that the five most important features were acuity, arrival trans portation, age, shock index, and respiratory rate. Machine learning models such as XGBoost may be used for sepsis prediction using only a smAll amount of data c onveniently collected in the ED triage stage."
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    New Findings from Hanyang University in the Area of Machine Learning Described ( Catching Robot: Predicting the Trajectory of a Rolling BAll Using Transformer)

    55-56页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Fresh data on artificial intelligence are presented in a new report. According to news reporting out of Seoul, South K orea, by NewsRx editors, research stated, "Various tasks in robotics such as pic k and place and catching flying/rolling objects have been studied in the literat ure. Previously, to accomplish such tasks, it was necessary to detect the positi on of the object using a Sobel detector, a marker, or a stereo method and then p redict the trajectory of the object through the model-based Kalman filter." Financial supporters for this research include Korea Institute of Energy Technol ogy Evaluation And Planning (Ketep) And The Ministry of Trade, Industry & Energy (Motie) of The Republic of Korea; National Research Foundation; Korean Go vernment.
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    National University Hospital Reports Findings in Artificial Intelligence (Oncolo gic Applications of Artificial Intelligence and Deep Learning Methods in CT Spin e Imaging-A Systematic Review)

    56-56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 Singapore, Sing apore, by NewsRx editors, research stated, "In spinal oncology, integrating deep learning with computed tomography (CT) imaging has shown promise in enhancing d iagnostic accuracy, treatment planning, and patient outcomes. This systematic re view synthesizes evidence on artificial intelligence (AI) applications in CT ima ging for spinal tumors." Funders for this research include MOH/NMRC, Singapore, Singapore Ministry of Hea lth National Medical Research Council. Our news journalists obtained a quote from the research from National University Hospital, "A PRISMAguided search identified 33 studies: 12 (36.4% ) focused on detecting spinal malignancies, 11 (33.3%) on classific ation, 6 (18.2%) on prognostication, 3 (9.1%) on treat ment planning, and 1 (3.0%) on both detection and classification. O f the classification studies, 7 (21.2%) used machine learning to di stinguish between benign and malignant lesions, 3 (9.1%) evaluated tumor stage or grade, and 2 (6.1 %) employed radiomics for biomarker classification. Prognostic studies included three (9.1%) that pred icted complications such as pathological fractures and three (9.1%) that predicted treatment outcomes. AI's potential for improving workflow effici ency, aiding decision-making, and reducing complications is discussed, along wit h its limitations in generalizability, interpretability, and clinical integratio n. Future directions for AI in spinal oncology are also explored."
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    University of Tehran Researchers Yield New Data on Machine Learning (Enhancing r eferences evapotranspiration forecasting with teleconnection indices and advance d machine learning techniques)

    57-57页
    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 ar tificial intelligence. According to news originating from the University of Tehr an by NewsRx correspondents, research stated, "After precipitation, reference ev apotranspiration (ETO) plays a crucial role in the hydrological cycle as it quan tifies water loss. ETO significantly impacts the water balance and holds great i mportance at the basin level because of the spatial distribution of managing wat er resources." Our news journalists obtained a quote from the research from University of Tehra n: "Large scale teleconnection indices (LSTIs) play a vital role by influencing climatic variables and can be pivotal in determining ETO and its predictive vari ables. This study aimed to model and forecast annual ETO in Iran's basins by uti lizing LSTIs and employing various machine learning models (MLMs) such as least squares support vector machine, generalized regression neural network, multi-lin ear regression (MLR), and multi-layer perceptron (MLP). InitiAlly, climate data from 122 synoptic stations covering six and 30, main and sub basins were collect ed, and annual ETO values were computed using the Food and Agriculture Organizat ion 56 (PMF 56) Penman-Monteith equation. The correlations between these values and 37 LSTIs were examined within lead times ranging from 7 to 12 months. Throug h a stepwise approach, the most influential predictor indices (LSTIs) were selec ted as input datasets for the MLMs. The findings revealed the significant influe nce of factors such as carbon dioxide (CO2), Atlantic multidecadal oscillation, Atlantic Meridional Mode, and East Atlantic on annual ETO."
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