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    Study Data from Jerash University Provide New Insights into Machine Learning (Ma chine learning-driven web-post buckling resistance prediction for high-strength steel beams with elliptically-based web openings)

    56-56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 reporting out of Jerash, Jordan, by New sRx editors, research stated, "The use of periodical elliptically-based web (EBW ) openings in high strength steel (HSS) beams has been increasingly popular in r ecent years mainly because of the high strength-to-weight ratio and the reductio n in the floor height as a result of allowing different utility services to pass through the web openings." Our news editors obtained a quote from the research from Jerash University: "How ever, these sections are susceptible to web-post buckling (WPB) failure mode and therefore it is imperative that an accurate design tool is made available for p rediction of the web-post buckling capacity. Therefore, the present paper aims t o implement the power of various machine learning (ML) methods for prediction of the WPB capacity in HSS beams with (EBW) openings and to assess the performance of existing analytical design model. For this purpose, a numerical model is dev eloped and validated with the aim of conducting a total of 10,764 web-post finit e element models, considering S460, S690 and S960 steel grades. This data is emp loyed to train and validate different ML algorithms including Artificial Neural Networks (ANN), Support Vector Machine Regression (SVR) and Gene Expression Prog ramming (GEP). Finally, the paper proposes new design models for WPB resistance prediction. The results are discussed in detail, and they are compared with the numerical models and the existing analytical design method."
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    Shijiazhuang University Researcher Provides New Study Findings on Machine Learni ng (An Intelligent Fault Diagnosis Algorithm for Vehicle Internal Combustion Eng ines Based on Instantaneous Speed for a Smart City)

    57-57页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Data detailed on artificial intelligence have bee n presented. According to news reporting out of Shijiazhuang University by NewsR x editors, research stated, "Affected by interference factors such as Gaussian n oise, the traditional methods have the problems of inaccurate diagnosis results of unsteady vibration signals, high uncertainty of fault diagnosis, and low over all fault diagnosis accuracy." Our news journalists obtained a quote from the research from Shijiazhuang Univer sity: "In this paper, a fault diagnosis algorithm of vehicle internal combustion engine based on instantaneous speed and machine learning is proposed. The insta ntaneous speed is measured by the hardware method. According to the processing r esults of instantaneous speed, the unsteady vibration signal of the vehicle inte rnal combustion engine is analyzed, and the principal components of unsteady vib ration are separated to suppress the interference of Gaussian strong noise. The running state of the vehicle internal combustion engine is identified by the wav elet transform method." According to the news reporters, the research concluded: "According to the ident ification results, the fault diagnosis of the vehicle internal combustion engine is realized by the twin support vector machine classification algorithm in mach ine learning. The experimental results show that the minimum uncertainty coeffic ient of fault diagnosis in this algorithm is 0.08, the accuracy of the unsteady vibration signal diagnosis is higher, and the overall accuracy of fault diagnosi s is lower."
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    Northeastern University Reports Findings in Cervical Cancer (CAISHI: A benchmark histopathological H&E image dataset for cervical adenocarcinoma in situ identification, retrieval and few-shot learning evaluation)

    58-58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 - Cervical Ca ncer is the subject of a report. According to news reporting originating from Li aoning, People's Republic of China, by NewsRx correspondents, research stated, " A benchmark histopathological Hematoxylin and Eosin (H&E) image dat aset for Cervical Adenocarcinoma (CAISHI), containing 2240 histopathological ima ges of Cervical Adenocarcinoma (AIS), is established to fill the current data ga p, of which 1010 are images of normal cervical glands and another 1230 are image s of cervical AIS. The sampling method is endoscope biopsy." Our news editors obtained a quote from the research from Northeastern University, "Pathological sections are obtained by H&E staining from Shengjin g Hospital, China Medical University. These images have a magnification of 100 a nd are captured by the Axio Scope. A1 microscope. The size of the image is 3840 x 2160 pixels, and the format is ‘.png'. The collection of CAISHI is subject to an ethical review by China Medical University with approval number 2022PS841K. T hese images are analyzed at multiple levels, including classification tasks and image retrieval tasks. A variety of computer vision and machine learning methods are used to evaluate the performance of the data. For classification tasks, a v ariety of classical machine learning classifiers such as -means, support vector machines (SVM), and random forests (RF), as well as convolutional neural network classifiers such as Residual Network 50 (ResNet50), Vision Transformer (ViT), I nception version 3 (Inception-V3), and Visual Geometry Group Network 16 (VGG-16), are used. In addition, the Siamese network is used to evaluate few-shot learni ng tasks. In terms of image retrieval functions, color features, texture feature s, and deep learning features are extracted, and their performances are tested. CAISHI can help with the early diagnosis and screening of cervical cancer."
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    Researchers at Jeju National University Target Machine Learning (Comparison of W ave Prediction and Performance Evaluation in Korea Waters based on Machine Learn ing)

    59-60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 artificial in telligence have been published. According to news originating from Jeju National University by NewsRx correspondents, research stated, "Waves are a complex phen omenon in marine and coastal areas, and accurate wave prediction is essential fo r the safety and resource management of ships at sea." Our news correspondents obtained a quote from the research from Jeju National Un iversity: "In this study, three types of machine learning techniques specialized in nonlinear data processing were used to predict the waves of Korea waters. An optimized algorithm for each area is presented for performance evaluation and c omparison. The optimal parameters were determined by varying the window size, an d the performance was evaluated by comparing the mean absolute error (MAE). All the models showed good results when the window size was 4 or 7 d, with the gated recurrent unit (GRU) performing well in all waters. The MAE results were within 0.161 m to 0.051 m for significant wave heights and 0.491 s to 0.272 s for peri ods. In addition, the GRU showed higher prediction accuracy for certain data wit h waves greater than 3 m or 8 s, which is likely due to the number of training p arameters."
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    Data on Artificial Intelligence Reported by Prannoy Paul and Colleagues (The Ris e of Artificial Intelligence: Implications in Orthopedic Surgery)

    59-59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 originating in Kerala, India, by NewsRx editors, the research stated, "Artificial intelligence (AI) is slowly making its way into all domains and medicine is no exception. AI is alre ady proving to be a promising tool in the health-care field." The news reporters obtained a quote from the research, "With respect to orthoped ics, AI is already under use in diagnostics as in fracture and tumor detection, predictive algorithms to predict the mortality risk and duration of hospital sta y or complications such as implant loosening and in real-time assessment of post -operative rehabilitation. AI could also be of use in surgical training, utilizi ng technologies such as virtual reality and augmented reality. However, clinicia ns should also be aware of the limitations of AI as validation is necessary to a void errors." According to the news reporters, the research concluded: "This article aims to p rovide a description of AI and its subfields, its current applications in orthop edics, the limitations, and its future prospects." For more information on this research see: The Rise of Artificial Intelligence: Implications in Orthopedic Surgery. Journal of Orthopaedic Case Reports, 2024;14(2):1-4.
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    Research from Beijing University of Technology Broadens Understanding of Machine Learning (Using Machine Learning and Finite Element Analysis to Extract Tractio n-Separation Relations at Bonding Wire Interfaces of Insulated Gate Bipolar ...)

    60-61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 reporting from Beijing, People's Repu blic of China, by NewsRx journalists, research stated, "For insulated gate bipol ar transistor (IGBT) modules using wire bonding as the interconnection method, t he main failure mechanism is cracking of the bonded interface. Studying the mech anical properties of the bonded interface is crucial for assessing the reliabili ty of IGBT modules." Financial supporters for this research include National Natural Science Foundati on of China. Our news correspondents obtained a quote from the research from Beijing Universi ty of Technology: "In this paper, first, shear tests are conducted on the bonded interface to test the bonded interface's strength. Then, finite element-cohesiv e zone modeling (FE-CZM) is established to describe the mechanical behavior of t he bonded interface. A novel machine learning (ML) architecture integrating a co nvolutional neural network (CNN) and a long short-term memory (LSTM) network is used to identify the shape and parameters of the traction separation law (TSL) o f the FE-CZM model accurately and efficiently. The CNN-LSTM architecture not onl y has excellent feature extraction and sequence-data-processing abilities but ca n also effectively address the long-term dependency problem. A total of 1800 set s of datasets are obtained based on numerical computations, and the CNN-LSTM arc hitecture is trained with load-displacement (F-d) curves as input parameters and TSL shapes and parameters as output parameters. The results show that the error rate of the model for TSL shape prediction is only 0.186%."
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    Researchers from China University of Mining and Technology Describe Findings in Robotics (Trajectory Planning and Control of Large Robotic Excavators Based On I nclination-displacement Mapping)

    61-62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 Robotics is the subjec t of a report. According to news reporting originating from Xuzhou, People's Rep ublic of China, by NewsRx correspondents, research stated, "This paper proposes a trajectory planning and control method for heavy-duty manipulators based on in clinationdisplacement mapping. In this method, inclination sensors are employed to detect the excavator's orientation." Funders for this research include National Natural Science Foundation of China ( NSFC), Natural Science Foundation of Jiangsu Province, Priority Academic Program Development of Jiangsu Higher Education Institutions. Our news editors obtained a quote from the research from the China University of Mining and Technology, "By establishing a mapping relationship between joint in clination and cylinder displacement, the cylinder displacement is indirectly der ived. Subsequently, considering the typical excavation operation process, the sp atial trajectory of the excavator bucket is planned. The displacement of the dri ving cylinder is determined using kinematic inverse solutions and cubic polynomi als. The cylinder displacement is then controlled using a position-velocity (PV) strategy. This method is tested on a 95-ton large excavator, and the results de monstrate several advantages, including easy installation, low maintenance requi rements, smooth trajectory planning, and high tracking accuracy. These qualities make it suitable for the demands of intelligent operations in large excavators."
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    Findings from Eindhoven University of Technology Yields New Data on Robotics (So rotoki: a Matlab Toolkit for Design, Modeling, and Control of Soft Robots)

    62-63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 Robotics is the subjec t of a report. According to news reporting originating from Eindhoven, Netherlan ds, by NewsRx correspondents, research stated, "In this paper, we present Soroto ki, an open-source toolkit in MATLAB that offers a comprehensive suite of tools for the design, modeling, and control of soft robots. The complexity involved in researching and building soft robots often stems from the interconnectedness of design and control aspects, which are rarely addressed together as a unified pr oblem." Financial support for this research came from Netherlands Organization for Scien tific Research (NWO). Our news editors obtained a quote from the research from the Eindhoven Universit y of Technology, "To address such complex interdependencies in soft robotics, th e Sorotoki toolkit provides a comprehensive and modular programming environment composed of seven Object-Oriented classes. These classes are designed to work to gether to solve a wide range of soft robotic problems, offering versatility and flexibility for its users. We provide here a comprehensive overview of the Sorot oki software architecture to highlight its usage and applications. The details a nd interconnections of each module are thoroughly described, collectively explai ning how to gradually introduce modeling complexity for various soft robotic sce narios. The effectiveness of Sorotoki is also demonstrated through a range of ca se studies, including novel problem scenarios and established works widely recog nized in the soft robotics community. These case studies cover a broad range of research problems, including: inverse design of soft actuators, passive and acti ve soft locomotion, object manipulation with soft grippers, meta-materials, mode l reduction, model-based control of soft robots, and online shape estimation. Ad ditionally, the toolkit provides access to four open-hardware soft robotic syste ms that can be fabricated using commercially available 3D printers."
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    Study Findings from New Jersey Institute of Technology Update Knowledge in Machi ne Learning (A Machine Learning Approach to Predict Relative Residual Strengths of Recycled Aggregate Concrete after Exposure to High Temperatures)

    63-64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 new report. According to news reporting originating from N ewark, New Jersey, by NewsRx correspondents, research stated, "In recent years, there has been a heightened focus among researchers and policymakers on assessin g the environmental impact and sustainability of human activities." The news reporters obtained a quote from the research from New Jersey Institute of Technology: "In this context, the reutilization of construction materials, pa rticularly recycled aggregate concrete, has emerged as an environmentally friend ly choice in construction projects, gaining significant traction. This study add resses the critical need to investigate the mechanical properties of recycled ag gregate concrete under diverse extreme scenarios. Conducting an extensive litera ture review, key findings were synthesized on the relative residual strength of recycled aggregate concrete following exposure to high temperatures. Leveraging these insights, innovative hybrid machine learning models were developed, offeri ng practical equations and model trees for predicting the relative residual comp ressive strength, flexural strength, elasticity modulus, and splitting tensile s trength of recycled aggregate concrete post high temperature exposure. Uncertain ty analysis was performed on each model to assess the reliability, while sensiti vity analysis was performed to find out the significance of each input variable for each predictive model. This paper presents interpretable models achieving hi gh levels of performance, with R2 values of 0.91, 0.94, 0.9, and 0.96 for predic ting the relative residual compressive strength, flexural strength, modulus of e lasticity, and splitting tensile strength of RCA concrete exposed to high temper atures, respectively."
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    West China Hospital of Sichuan University Reports Findings in Arthroplasty (Safe ty and Effectiveness of Robotic-Arm Assisted Total Knee Arthroplasty)

    64-65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 Surgery - Arthroplasty is the subject of a report. According to news originating from Chengdu, People' s Republic of China, by NewsRx correspondents, research stated, "We investigated the advantages of robotic arm-assisted total knee arthroplasty (raTKA) over con ventional manual TKA (cmTKA) by comprehensively comparing patients who received raTKA and cmTKA in terms of postoperative pain, function, imaging assessment, an d trauma to the body. This study investigated the efficacy and safety of raTKA i n patients using the YUANHUA-TKA system." Financial support for this research came from National Natural Science Foundatio n of China. Our news journalists obtained a quote from the research from the West China Hosp ital of Sichuan University, "In a prospective, randomized single-blind trial, 60 patients undergoing primary unilateral TKA from October 2020 to December 2020 w ere randomly assigned to either raTKA or cmTKA. Clinical evaluation, including t he time of osteotomy and prosthesis model testing, the total operation time, the visual analogue scale at rest, VAS in motion, opioid consumption, white blood c ell count, neutrophil ratio, erythrocyte sedimentation rate, C-reactive protein (CRP), passive and active range of motion (pROM, aROM), Western Ontario and McMa ster Universities Arthritis Index (WOMAC [stiffness, pain, an d function]) score, gait analysis, keen society score (KSS), adverse events, and blood loss were collected by the project nurse, as well as t he imaging evaluation, including the lateral tibia component angle (LTC), fronta l femoral component angle, frontal tibia component angle (FTC), lateral femoral component angl, and hip-knee-ankle angle (HKA). The student t-test (or the Wilco xon signed-rank test) and the ch - test (or the Fisher exact test) were used to d etermine differences in categorical variables. No significant difference was fou nd between the two groups in pain throughout the whole follow-up period. On the third day postoperatively, the erythrocyte sedimentation rate in the cmTKA group was significantly higher (p = 0.02), as well as the CRP (p = 0.04). No signific ant difference was found in the WOMAC stiffnes score or pROM. However, the aROM and the flexion range when walking (FRW) were significantly better in the raTKA group throughout the trial (p <0.05). The KSS at the 1-mon th follow-up and the WOMAC function score at the 1-year follow-up were both sign ificantly better in the raTKA group (p <0.05). The HKA and the LTC in the raTKA group closer to the ideal angle, and the difference betwee n the groups was significant (p <0.05). The total operatio n time of the raTKA group was significantly longer (p = 0.001). The intraoperati ve blood loss had no significant difference in the two groups."
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