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    Hangzhou Dianzi University Reports Findings in Machine Learning (Behavioral toxi cological tracking analysis of Drosophila larvae exposed to polystyrene micropla stics based on machine learning)

    19-20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 Machine Learning is th e subject of a report. According to news originating from Hangzhou, People’s Rep ublic of China, by NewsRx correspondents, research stated, “Microplastics, as a pivotal concern within plastic pollution, have sparked widespread apprehension d ue to their ubiquitous presence. Recent research indicates that these minuscule plastic particles may exert discernible effects on the locomotor capabilities an d behavior of insect larvae.” Our news journalists obtained a quote from the research from Hangzhou Dianzi Uni versity, “This study focuses on the impact of polystyrene microplastics (PS-MPs) on the behavior of Drosophila melanogaster larvae, utilizing fruit flies as a model organism. Kinematic analysis methods w ere employed to assess and extrapolate the toxic effects of PS-MPs on the larvae . Drosophila larvae were exposed to varying concentrations (Control, 0.1 g/L, 1 g/L, 10 g/L, 20 g/L) of 5 mm PS-MPs during their developmental stages. The study involved calculating and evaluating parameters such as the proportion of larvae reaching the edge, distance covered, velocity, and angular velocity within a 5- min timeframe. Across different concentrations, Drosophila larvae exhibit differ ential degrees of impaired motor function and disrupted locomotor orientation. T he proportion of larvae reaching the edge decreased, velocity significantly decl ined, and angular velocity exhibited a notable increase. These findings strongly suggest that when exposed to a PS-MPs environment, Drosophila larvae exhibit sl ower movement, increased angular rotation per unit time, leading to a reduction in the proportion of larvae reaching the edge.”
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    New Findings from Incheon National University Describe Advances in Robotics (End -to-End Path Planning Under Linear Temporal Logic Specifications)

    20-21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 robotics are presented i n a new report. According to news reporting from Incheon, South Korea, by NewsRx journalists, research stated, “This paper presents a novel deep learning framew ork for robotic path planning that seamlessly integrates Linear Temporal Logic ( LTL) with trajectory optimization to meet mission specifications efficiently.” Funders for this research include Incheon National University Research Grant, in 2023; National Research Foundation of Korea; Korean Government.
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    China University of Petroleum (East China) Reports Findings in Machine Learning (Prediction Method for Formation Pore Pressure Based On Transfer Learning)

    21-22页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Data detailed on Machine Learning have been prese nted. According to news reporting out of Qingdao, People’s Republic of China, by NewsRx editors, research stated, “Pore pressure prediction of drilled wells is the recognition of formation information using logging and other actual drilling data after drilling is completed, which is of great significance in improving t he mastery of regional formation information and reducing the potential risks of the engineering program of wells to be drilled. Traditional formation pressure prediction methods rely on manual experience and are difficult to cope with comp lex formations.” Financial supporters for this research include National Natural Science Foundati on of China (NSFC), Natural Science Foundation of Shandong Province, Taishan Sch olars Program of Shandong Province, Youth Innovation and Technology Support Prog ram for Shandong Provincial Universities.
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    New Artificial Intelligence Study Findings Reported from University of Pittsburg h (Utilizing Artificial Intelligence In Academic Writing: an In-depth Evaluation of a Scientific Review On Fertility Preservation Written By Chatgpt-4)

    22-23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 reporting out of Pittsburgh, P ennsylvania, by NewsRx editors, research stated, “To evaluate the ability of Cha tGPT-4 to generate a biomedical review article on fertility preservation.Methods ChatGPT-4 was prompted to create an outline for a review on fertility preservat ion in men and prepubertal boys. The outline provided by ChatGPT-4 was subsequen tly used to prompt ChatGPT-4 to write the different parts of the review and prov ide five references for each section.” Financial support for this research came from NIH Eunice Kennedy Shriver Nationa l Institute of Child Health & Human Development (NICHD).
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    Beijing Jishuitan Hospital Reports Findings in Arthroplasty (Developing a Machin e-Learning Predictive Model for Retention of Posterior Cruciate Ligament in Pati ents Undergoing Total Knee Arthroplasty)

    23-24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 reporting originating from Beijin g, People’s Republic of China, by NewsRx correspondents, research stated, “Predi cting whether the posterior cruciate ligament (PCL) should be preserved during t otal knee arthroplasty (TKA) procedures is a complex task in the preoperative ph ase. The choice to either retain or excise the PCL has a substantial effect on t he surgical outcomes and biomechanical integrity of the knee joint after the ope ration.” Our news editors obtained a quote from the research from Beijing Jishuitan Hospi tal, “To enhance surgeons’ ability to predict the removal and retention of the P CL in patients before TKA, we developed machine learning models. We also identif ied significant feature factors that contribute to accurate predictions during t his process. Patients’ data on TKA continuously performed by a single surgeon wh o had intended initially to undergo implantation of cruciate-retaining (CR) pros theses was collected. During the sacrifice of PCL, we utilized anterior-stabiliz ed (AS) tibial bearings. The dataset was split into CR and AS categories to form distinct groups. Relevant information regarding age, gender, body mass index (B MI), the affected side, and preoperative diagnosis was extracted by reviewing th e medical records of the patients. To ensure the authenticity of the research, a n initial step involved capturing X-ray images before the surgery. These images were then analyzed to determine the height of the medial condyle (MMH) and later al condyle (LMH), as well as the ratios between MLW and MMH and MLW and LMH. Add itionally, the insall-salvati index (ISI) was calculated, and the severity of an y varus or valgus deformities was assessed. Eight machine-learning methods were developed to predict the retention of PCL in TKA. Risk factor analysis was perfo rmed using the SHApley Additive exPlanations method. A total of 307 knee joints from 266 patients were included, among which there were 254 females and 53 males . A stratified random sampling technique was used to split patients in a 70:30 r atio into a training dataset and a testing dataset. Eight machine-learning model s were trained using data feeding. Except for the AUC of the LGBM Classifier, wh ich is 0.70, the AUCs of other machine learning models are all lower than 0.70. In importance-based analysis, ISI, MMH, LMH, deformity, and age were confirmed a s important predictive factors for PCL retention in operations. The LGBM Classif ier model achieved the best performance in predicting PCL retention in TKA.”
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    Data on Robotics Reported by Researchers at Sun Yat-sen University (Orb-neurosla m: a Brain-inspired 3-d Slam System Based On Orb Features)

    24-25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 Robotics are presented i n a new report. According to news reporting originating in Shenzhen, People’s Re public of China, by NewsRx journalists, research stated, “Intelligent navigation is a fundamental technology that enables unmanned systems to achieve autonomy i n the intelligent era. However, existing navigation schemes suffer from high com putational complexity and power consumption, as well as low robustness in comple x or unknown environments.” Financial support for this research came from National Natural Science Foundatio n of China (NSFC). The news reporters obtained a quote from the research from Sun Yat-sen Universit y, “To address these challenges, this article proposes a novel 3-D brain-inspire d simultaneous localization and mapping (SLAM) method, called oriented FAST and rotated BRIEF (ORB)-NeuroSLAM, based on the ORB features. The proposed method ta kes inspiration from the robust and low-power navigation capabilities of humans and animals. The ORB-NeuroSLAM leverages the ORB features of camera images to co mpute robot self-motion and visual cues. Then, continuous attractor neural netwo rks (CANNs) model multilayered head-direction cells and 3-D grid cells that exis t in animal brains. These cells are utilized jointly to represent the robot pose s. Efficient and robust methods for loop closure detection and experience map co nstruction were also developed. The proposed method was verified on ten KITTI da ta sets, and experimental results demonstrate that it outperforms state-of-the-a rt brain-inspired SLAM methods in terms of accuracy and efficiency.”
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    Research from Inner Mongolia University Has Provided New Study Findings on Suppo rt Vector Machines (Measurement of body size parameters and body weight predicti on in beef cattle based on image analysis)

    25-26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 have been pub lished. According to news reporting from Hohhot, People’s Republic of China, by NewsRx journalists, research stated, “The body size parameter of cattle is an im portant index reflecting the growth and development and health condition of catt le. The traditional manual contact measurement is not only a large workload and difficult to measure, but also prone to problems such as affecting the normal li fe habits of cattle.” The news reporters obtained a quote from the research from Inner Mongolia Univer sity: “In this paper, we address this problem by proposing a contactless body si ze measurement method for cattle based on machine vision. Firstly, the cattle is confined to a fixed space using a position-limiting device, and images of the b ody of the cattle are taken from three directions: top, left, and right, using m ultiple cameras. Secondly, the image is segmented using a fuzzy clustering algor ithm based on neighborhood adaptive local spatial information improvement, and t he image is processed to extract the contour images of the top view and side vie w. The key points of body measurements were extracted using interval division an d curvature calculation for the side view images, and the key point information was extracted using skeleton extraction and pruning for the top view images, whi ch realized the measurements of body height(BH), rump height(RH), body slanting length(BSL), and abdominal circumference(AC) parameters of the cattle. The corre lation between body size and weight data obtained by contactless methods was inv estigated and the modeled using one-factor linear regression, one-factor nonline ar regression, multivariate stepwise regression, RBF network fitting, BP neural network fitting, support vector machine, and particle swarm optimization-based s upport vector machine methods, respectively. Information on body size parameters was collected from 137 cattles, and the results showed that the maximum errors between the measured and actual values of BH, RH, BSL and AC were 5.0% , 4.4%, 3.6%, and 5.5%, respectively. Cor relation of BH, RH, BSL and AC with weight obtained by non-contact methods was > 0.75.”
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    Universidad Militar Nueva Granada Researchers Target Artificial Intelligence (Fr om Trends to Experiences: Co-Creation with Generative Artificial Intelligence in Developing Interactive Multimedia Applications)

    26-26页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New study results on artificial intell igence have been published. According to news reporting out of the Universidad M ilitar Nueva Granada by NewsRx editors, research stated, “The emergence of gener ative artificial intelligence (GenAI) tools has ushered in new possibilities for creating diverse content, encompassing text, images, sound, videos, 3D objects, and even code for programming languages.” Our news reporters obtained a quote from the research from Universidad Militar N ueva Granada: “Within this dynamic landscape, both significant challenges and op portunities arise in the field of interactive multimedia application development . It is imperative that the evolving trends in the utilization of these tools me tamorphose into experiences that not only reflect but also facilitate the replic ation of models, methods, and development approaches. This paper contributes to the academic discourse by presenting three distinct development experiences. Eac h experience represents the initial steps of an incremental exploration process, thus contributing to exploratory research in this domain. These experiences are presented as case studies, with the first one analyzed retrospectively, and the latter two deliberately designed to scrutinize the challenges and opportunities associated with integrating generative artificial intelligence into the softwar e development process, playing a significant role in a co-creation process. The results presented are qualitative, with detailed independent analyses for each c ase study, offering a comprehensive description of the findings.”
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    Studies from Chinese Academy of Sciences Have Provided New Information about Com putational Intelligence (Compensation Atmospheric Scattering Model and Two-branc h Network for Single Image Dehazing)

    27-27页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Research findings on Machine Learning - Computational Intelligence are discussed in a new report. According to news re porting from Shenyang, People’s Republic of China, by NewsRx journalists, resear ch stated, “Most existing dehazing networks rely on synthetic hazy-clear image p airs for training, and thus fail to work well in real-world scenes. In this pape r, we deduce a reformulated atmospheric scattering model for a hazy image and pr opose a novel lightweight two-branch dehazing network.” Financial support for this research came from National Natural Science Foundatio n of China (NSFC).
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    National Cheng Kung University Researcher Provides New Insights into Robotics (T opology Optimization of a Compliant Constant- Force End Effector for Robotic Oper ations Over Uneven Surfaces)

    28-29页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Data detailed on robotics have been pr esented. According to news reporting from Tainan, Taiwan, by NewsRx journalists, research stated, “A compliant constant-force mechanism (CCFM) is a specific typ e of compliant mechanism that serves as a passive force regulation device.” Financial supporters for this research include Ministry of Science And Technolog y, Taiwan. The news reporters obtained a quote from the research from National Cheng Kung U niversity: “When subjected to a load, it undergoes deformation, resulting in an almost consistent output force regardless of changes in input displacement. Trad itional methods used to design CCFMs typically rely on either stiffness combinat ion or parametric optimization based on existing design configurations. To enabl e the direct synthesis of CCFMs according to desired boundary conditions, this s tudy proposes a systematic topology optimization method. This method includes a new morphology-based scheme designed to ensure the connectivity of the topologic al results, thereby achieving this objective. Using this approach, a CCFM suitab le for end effector applications is designed and manufactured through 3D printin g. Four of these CCFMs are then utilized to create an innovative compliant const ant-force end effector for robotic operations on uneven surfaces.”
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