查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-New research on intelligent systems is the subjec t of a new report. According to news originating from Selangor, Malaysia, by New sRx editors, the research stated, "In recent years, the field of data analytics has witnessed a surge in innovative techniques to handle the ever-increasing vol ume and complexity of data. Among these, nature-inspired algorithms have gained significant attention due to their ability to efficiently mimic natural processe s and solve intricate problems." Our news reporters obtained a quote from the research from Universiti Kebangsaan Malaysia: "One such algorithm, the symbiotic organisms search (SOS) Algorithm, has emerged as a promising approach for clustering and predictive analytics task s, drawing inspiration from the symbiotic relationships observed in biological e cosystems. Metaheuristics such as the SOS have been frequently employed in clust ering to discover suitable solutions for complicated issues. Despite the numerou s research works on clustering and SOS-based predictive techniques, there have b een minimal secondary investigations in the field. The aim of this study is to f ill this gap by performing a systematic literature review (SLR) on SOS-based clu stering models focusing on various aspects, including the adopted clustering app roach, feature selection approach, and hybridized algorithms combining K-means a lgorithm with different SOS algorithms. This review aims to guide researchers to better understand the issues and challenges in this area. The study assesses th e unique articles published in journals and conferences over the last ten years (2014-2023). After the abstract and full-text eligibility analysis, a limited nu mber of articles were considered for this SLR."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Investigators publish new report on artificial in telligence. According to news originating from the University of Florida by News Rx correspondents, research stated, "A key challenge facing the use of machine l earning (ML) in organizational selection settings (e.g., the processing of loan or job applications) is the potential bias against (racial and gender) minoritie s." Financial supporters for this research include National Science Foundation. Our news editors obtained a quote from the research from University of Florida: "To address this challenge, a rich literature of Fairness-Aware ML (FAML) algori thms has emerged, attempting to ameliorate biases while maintaining the predicti ve accuracy of ML algorithms. Almost all existing FAML algorithms define their o ptimization goals according to a selection task, meaning that ML outputs are ass umed to be the final selection outcome. In practice, though, ML outputs are rare ly used as-is. In personnel selection, for example, ML often serves a support ro le to human resource managers, allowing them to more easily exclude unqualified applicants. This effectively assigns to ML a screening rather than a selection t ask. It might be tempting to treat selection and screening as two variations of the same task that differ only quantitatively on the admission rate."
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 originating in Xi'an, People's Republic of C hina, by NewsRx journalists, research stated, "In modern industrial environments , robots are expected to work close to human operators collaborating with them i n completing various tasks, e.g., collaborative assembly or delivery of objects. However, it is difficult to take task optimization into account under the premi se of ensuring safety in a human-involved dynamic environment." Financial supporters for this research include National Natural Science Foundati on of China (NSFC), National Key R&D Program of China, China Schola rship Council. The news reporters obtained a quote from the research from Xi'an Jiaotong Univer sity, "Therefore, a real-time hierarchical control method containing two hierarc hical optimal controllers with complementary functions is proposed to tackle the above problems. The upper-layer Model Predictive Controller aims at performing primary tasks, such as end-effector pose tracking, singularity, and joint limit avoidance. It is formulated as a Bayesian Inference problem with a Gaussian proc ess prior and an exponential likelihood function. The resulting maximum a poster iori estimation problem can be solved efficiently using the Matrix- Scaled Stein Variational Gradient Descent and GPU. The upper-layer optimal controller aims at performing primary tasks, such as end-effector pose tracking, singularity, and joint limit avoidance. The lower layer safety-critical controller, formulated as a constrained quadratic programming problem, is responsible for tracking the ou tput interpolation of the higher-layer controller while respecting the safety co nstraints constructed in the form of Stochastic Control Barrier Functions. Both of the optimal controllers run repeatedly but with different frequencies (upper- layer controller: 20 Hz, lower-layer controller: 40 Hz). The proposed method pro vides a solution to deal with both collision avoidance and task constraints."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Fresh data on Machine Learning are pre sented in a new report. According to news reporting originating in Lille, France , by NewsRx journalists, research stated, "The human Mediator complex subunit ME D25 binds transactivation domains (TADs) present in various cellular and viral p roteins using two binding interfaces, named H1 and H2, which are found on opposi te sides of its ACID domain. Here, we use and compare deep learning methods to c haracterize human MED25-TAD interfaces and assess the predicted models to publis hed experimental data." The news reporters obtained a quote from the research from the University of Lil le, "For the H1 interface, AlphaFold produces predictions with high-reliability scores that agree well with experimental data, while the H2 interface prediction s appear inconsistent, preventing reliable binding modes. Despite these limitati ons, we experimentally assess the validity of MED25 interface predictions with t he viral transcriptional activators Lana-1 and IE62."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Drugs and Therapies-Antioxidants is the subject of a report.According to news reporting originating in Padua, Italy, by NewsRx journalists, research stated, "Antioxidant peptides (AOPs) are highly valued in food and pharmaceutical industries due to their sign ificant role in human function. This study introduces a novel approach to identi fying robust AOPs using a deep generative model based on sequence representation ." The news reporters obtained a quote from the research, "Through filtration with a deep-learning classification model and subsequent clustering via the Butina cl uster algorithm, twelve peptides (GP1-GP12) with potential antioxidant capacity were predicted. Density functional theory (DFT) calculations guided the selectio n of six peptides for synthesis and biological experiments. Molecular orbital re presentations revealed that the HOMO for these peptides is primarily localized o n the indole segment, underscoring its pivotal role in antioxidant activity. All six synthesized peptides exhibited antioxidant activity in the DPPH assay, whil e the hydroxyl radical test showed suboptimal results. A hemolysis assay confirm ed the nonhemolytic nature of the generated peptides. Additionally, an in silic o investigation explored the potential inhibitory interaction between the peptid es and the Keap1 protein. Analysis revealed that ligands GP3, GP4, and GP12 indu ced significant structural changes in proteins, affecting their stability and fl exibility." According to the news reporters, the research concluded: "These findings highlig ht the capability of machine learning approaches in generating novel antioxidant peptides."
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 originating in Harbin, People's Republic of China, by NewsRx journalists, research stated, "This article proposes a stable a nd high-accuracy model-free calibration method for non-open robotic systems, whi ch can significantly improve the robot positional accuracy. Two improvements are made to the existing kriging-based error compensation method to achieve robustn ess/practicality enhancement goals."
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 are discussed in a new report. According to news reporting from Shanghai, People 's Republic of China, by NewsRx journalists, research stated, "Programming the s hape of soft smart materials is a challenging task due to the enormous design sp ace involved. In this study, we propose a novel approach to determine applied st imuli that enable the desired actuated shapes of soft smart materials." Funders for this research include National Key Research and Development Program of China, National Natural Science Foundation of China (NSFC), Key Research Proj ect of Zhejiang Lab.
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 originating in Arrasate Mondragon, Spain, by NewsRx journalists, research stated, "The manufacturing industry of the future requires innovative approaches to optimize operational efficiency and adaptabili ty. Integrating context-awareness into workflow management systems has emerged a s a promising avenue to enhance efficiency in modern manufacturing processes." Financial supporters for this research include Department of Education, Universi ties and Research of the Basque Government, Ikerketa Taldeak program.
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 from Nagpur, India, by NewsRx journalists, research stated, "AI has shown promise in automating and im proving various tasks, including medical image analysis. Distal humerus fracture s are a critical clinical concern that requires early diagnosis and treatment to avoid complications." The news correspondents obtained a quote from the research from the Department o f Orthopedics, "The standard diagnostic method involves X-ray imaging, but subtl e fractures can be missed, leading to delayed or incorrect diagnoses. Deep learn ing, a subset of artificial intelligence, has demonstrated the ability to automa te medical image analysis tasks, potentially improving fracture identification a ccuracy and reducing the need for additional and cost-intensive imaging modaliti es (Schwarz et al. 2023). This study aims to develop a deep learning-based diagn ostic support system for distal humerus fractures using conventional X-ray image s. The primary objective of this study is to determine whether deep learning can provide reliable image-based fracture detection recommendations for distal hume rus fractures. Between March 2017 and March 2022, our tertiary hospital's PACS d ata were evaluated for conventional radiography images of the anteroposterior (A P) and lateral elbow for suspected traumatic distal humerus fractures.The data set consisted of 4931 images of patients seven years and older, after excluding paediatric images below seven years due to the absence of ossification centres. Two senior orthopaedic surgeons with 12 + years of experience reviewed and label led the images as fractured or normal. The data set was split into training sets (79.88%) and validation tests (20.1%). Image pre-proc essing was performed by cropping the images to 224 x 224 pixels around the capit ellum, and the deep learning algorithm architecture used was ResNet18. The deep learning model demonstrated an accuracy of 69.14% in the validatio n test set, with a specificity of 95.89% and a positive predictive value (PPV) of 99.47%. However, the sensitivity was 61.49% , indicating that the model had a relatively high false negative rate. ROC analy sis showed an AUC of 0.787 when deep learning AI was the reference and an AUC of 0.580 when the most senior orthopaedic surgeon was the reference. The performan ce of the model was compared with that of other orthopaedic surgeons of varying experience levels, showing varying levels of diagnostic precision. The developed deep learningbased diagnostic support system shows potential for accurately di agnosing distal humerus fractures using AP and lateral elbow radiographs. The mo del's specificity and PPV indicate its ability to mark out occult lesions and ha s a high false positive rate."
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 reporting originating from Zhejiang, People's Republic of China, by NewsRx correspondents, research stated, "Collaborative robots are desi gned to not only work alongside humans but also adapt to new tasks quickly. To i mprove their absolute accuracy, self-calibration methods utilizing portable meas urement devices are cost-effective solutions." Funders for this research include National Key Research and Development Program of China, National Natural Science Foundation of China (NSFC), Key Research and Development Program of Zhejiang, China.
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