查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Research findings on robotics are disc ussed in a new report. According to news originating from Pontedera, Italy, by N ewsRx correspondents, research stated, "The development of functional microsyste ms and microrobots that have characterized the last decade is the result of a sy nergistic and effective interaction between the progress of fabrication techniqu es and the increased availability of smart and responsive materials to be employ ed in the latter." Funders for this research include European Union Horizon 2020 Research And Innov ation Program Under The Future And Emerging Technologies Open Grant 5DNANOPRINTI NG; European Union Horizon 2020 Excellent Science Program Under The Erc Starting Grant Celloids. Our news editors obtained a quote from the research from Italian Institute of Te chnology: "Functional structures on the microscale have been relevant for a vast plethora of technologies that find application in different sectors including a utomotive, sensing devices, and consumer electronics, but are now also entering medical clinics. Working on or inside the human body requires increasing complex ity and functionality on an ever-smaller scale, which is becoming possible as a result of emerging technology and smart materials over the past decades. In rece nt years, additive manufacturing has risen to the forefront of this evolution as the most prominent method to fabricate complex 3D structures. In this review, w e discuss the rapid 3D manufacturing techniques that have emerged and how they h ave enabled a great leap in microrobotic applications. The arrival of smart mate rials with inherent functionalities has propelled microrobots to great complexit y and complex applications. We focus on which materials are important for actuat ion and what the possibilities are for supplying the required energy."
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 Ma chine Learning. According to news reporting out of Chengdu, People's Republic of China, by NewsRx editors, research stated, "Subtle sandstone reservoirs are dif ficult to identify due to their weak seismic responses. Here, we propose to iden tify subtle sandstone reservoirs by an unsupervised machine learning-based multi -attribute fusion scheme using prestack seismic data." Financial supporters for this research include National Natural Science Foundati on of China (NSFC), Chengdu University of Technology Postgraduate Innovative Cul tivation Program, Technological Development for Sichuan Province, Natural Scienc e Foundation of Sichuan. Our news journalists obtained a quote from the research from the Chengdu Univers ity of Technology, "The proposed scheme carries out seismic attenuation gradient analysis and prestack simultaneous inversion to obtain the attributes that are sensitive to subtle channel sands, and uses them as the selected multiple attrib utes, and further employs a state-of-the-art unsupervised machine learning algor ithm, called isolation forest, for the multi-attribute anomaly detection and ana lysis to identify subtle sandstone reservoir. To the best of our knowledge, this is the first time to introduce the isolation forest unsupervised anomaly detect ion algorithm in the reservoir identification. Prestack simultaneous inversion c an use multi-angle and multi-scale information as constraints, and the attenuati on gradient reflects the body response of the reservoir. For the field seismic d ata from a subtle channel sandstone reservoir in the western Sichuan basin, Chin a, we found that the proposed scheme has good application effect in identifying subtle reservoirs. The application example demonstrates that the identified resu lts are highly consistent with the actual development results, illustrating the feasibility and effectiveness of this scheme on the characterization for dim spo t subtle sandstone reservoirs."
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 reporting out of Dalian, People's Repub lic of China, by NewsRx editors, research stated, "Metal-organic frameworks (MOF s) have been widely studied for Cr(VI) adsorption in water. Theoretically, numer ous MOFs can be synthesised by assembling diverse metals and ligands." Our news journalists obtained a quote from the research from Dalian Ocean Univer sity, "However, the traditional manual experimentation for screening high-perfor mance MOFs is resource-intensive and inefficient. A screening strategy for MOFs based on machine learning was proposed for the adsorption and removal of Cr(VI) from water. By collecting the characteristics of MOFs and the experimental param eters of Cr(VI) adsorption from the literature, a dataset was constructed to pre dict the adsorption performance. Among the six regression models, the model trai ned by the extreme gradient boosted tree algorithm had the best performance and was used to simulate the adsorption and screen potential high-performance adsorb ents. Structure-property analysis indicated that prepared MOF adsorbents with pr operties of 0.37 <largest cavity diameter <0.71 nm, 0.18 <pore volume <0.57 cm /g, 412 <specific surface area <1588 m/g, 0.43 <void fraction <0.62 will achieve enhanced adsorption of Cr(VI) in water. High-performance adsorbents wer e successfully screened using a combination of machine-learning prediction and a nalysis. Experiments were conducted to verify the exceptional adsorption capacit y of UiO-66 and MOF-801."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Biotechnology - Bioinf ormatics is the subject of a report. According to news originating from Hefei, P eople's Republic of China, by NewsRx correspondents, research stated, "In this s tudy, we investigated the internal relationship between the pathogenesis of diab etic kidney disease (DKD) and abnormal glucose and lipid metabolism to identify potential biomarkers for diagnosis and treatment and investigated the role of th e immune microenvironment of glucose and lipid metabolism disorders in the occur rence and progression of DKD. The chip datasets GSE104948 and GSE96804 from the Gene Expression Common Database (GEO) were merged using the ‘lima' and ‘sva' sof tware packages in R Software (4.2.3), and the merged dataset was used as the val idation set." Our news journalists obtained a quote from the research from the Anhui Universit y of Chinese Medicine, "The intersection between the differential genes of DKD a nd the glucose and lipid metabolism genes in the MSigDB database was identified, and a nomogram of the incidence risk of DKD was built using three machine learn ing methods, namely LASSO regression, support vector machine (SVM), and random f orest (RF), to validate the accuracy of the prediction model. Immune scores were conducted using the unsupervised clustering method, and patients were divided i nto two subgroups. The two subgroups were screened for differential genes for en richment analysis. The differential genes of patients diagnosed with DKD were cl ustered into two gene subgroups for co-expression analysis. In this study, we ut ilized the Cytoscape software to construct a network of interactions among key g enes. Using machine learning, a diagnostic model was developed with G6PC and HSD 17B14 as key factors. Enrichment analysis and immune scoring demonstrated that t he development of DKD was related to the imbalance in the microenvironment broug ht about by glucose lipid metabolism disorders."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Research findings on artificial intell igence are discussed in a new report. According to news reporting from Krasnoyar sk, Russia, by NewsRx journalists, research stated, "In the context of the 21st century and the fourth industrial revolution, the substantial proliferation of d ata has established it as a valuable resource, fostering enhanced computational capabilities across scientific disciplines, including physics." Our news journalists obtained a quote from the research from Siberian Federal Un iversity: "The integration of Machine Learning stands as a prominent solution to unravel the intricacies inherent to scientific data. While diverse machine lear ning algorithms find utility in various branches of physics, there exists a need for a systematic framework for the application of Machine Learning to the field . This review offers a comprehensive exploration of the fundamental principles a nd algorithms of Machine Learning, with a focus on their implementation within d istinct domains of physics. The review delves into the contemporary trends of Ma chine Learning application in condensed matter physics, biophysics, astrophysics , material science, and addresses emerging challenges. The potential for Machine Learning to revolutionize the comprehension of intricate physical phenomena is underscored."
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 from Henan, People's Republic of China, by NewsRx journalists, research stated, "The rapid advanceme nt of the internationalization process has led to the gradual labeling and commo ditization of commercial space, which is detached from the connotation of region al culture." Our news journalists obtained a quote from the research from Zhengzhou Universit y: "This paper establishes a commercial space layout model and a commercial spac e layout optimization design pro-cess based on Bayesian network, combined with th e geometric characteristics and layout attributes of commercial space layout. It also utilizes Simpson's index and kernel density analysis to explore the influe ncing factors of regional culture integration into commercial space design and f inally takes the design of comprehensive commercial space with regional characte ristics of culture in the Central Plains as a research case to specifically expl ore the feasibility of integrating regional cultural characteristics into commer cial space design. The results show that in the process of designing the commerc ial space of regional culture in the Central Plains, the audiovisual comfort rea ches its best when the space aspect ratio is about 1.42 and the color saturation is about 25.05."
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 artificial intelligence is now available. According to news reporting from Selangor, Malaysia, by NewsR x journalists, research stated, "This paper will introduce the application of ar tificial intelligence (AI), machine learning, and deep learning in art design an d visual arts, and how these technologies can be used to create unique souvenirs ." The news reporters obtained a quote from the research from University Putra Mala ysia: "In the field of art design, AI and machine learning can be used to automa tically generate artwork and patterns, providing more inspiration and creativity , and can also help artists better understand their audience and market. The app lication of deep learning in the field of visual arts includes image recognition , image classification, image generation, and so on." According to the news editors, the research concluded: "In the field of souvenir design, the use of AI and machine learning can help designers better understand market needs and consumer trends to create unique souvenirs. Taken together, th e application of AI, machine learning, and deep learning technologies has great potential and creativity in the fields of art design and souvenirs."
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. According to news reporting originating in Wenzhou, People's Republic of China, by NewsRx journalists, research stated, "With the development of land slide monitoring system, many attempts have been made to predict landslide failu re-time utilizing monitoring data of displacements. Classical models (e.g., Verh ulst, GM (1,1), and Saito models) that consider the characteristics of landslide displacement to determine the failuretime have been investigated extensively." Financial supporters for this research include Natural Science Foundation of Hun an Province, Special Fund for Safety Production Prevention and Emergency of Huna n Province, Research Project of Geological Bureau of Hunan Province, Fund of Wen zhou Municipal Science and Technology Bureau, Fundamental Research Funds for Cen tral Universities of the Central South University.
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Research findings on robotics are disc ussed in a new report. According to news originating from Xi'an Jiaotong Univers ity by NewsRx correspondents, research stated, "The Industrial Product Service S ystem (IPS2) is considered a sustainable and efficient business model, which has been gradually popularized in manufacturing fields since it can reduce costs an d satisfy customization." Funders for this research include National Key Research And Development Program of China. Our news journalists obtained a quote from the research from Xi'an Jiaotong Univ ersity: "However, a comprehensive design method for IPS2 is absent, particularly in terms of requirement perception, resource allocation, and service activity a rrangement of specific industrial fields. Meanwhile, the planning and scheduling of multiple parallel service activities throughout the delivery of IPS2 are als o in urgent need of resolution. This paper proposes a method containing service order design, service resource configuration, and service flow modeling to estab lish an IPS2 for robot-driven sanding processing lines."
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. According to news reporting from Pittsburgh, Pennsylvania, by NewsRx journalists, research stated, "The lifelong learning paradigm in machine learnin g is an attractive alternative to the more prominent isolated learning scheme no t only due to its resemblance to biological learning but also its potential to r educe energy waste by obviating excessive model re-training. A key challenge to this paradigm is the phenomenon of catastrophic forgetting." Financial supporters for this research include DSO National Laboratories, Canada CIFAR AI Chair, Natural Sciences and Engineering Research Council of Canada (NS ERC). The news correspondents obtained a quote from the research from Carnegie Mellon University, "With the increasing popularity and success of pre-trained models in machine learning, we pose the question: What role does pre-training play in lif elong learning, specifically with respect to catastrophic forgetting? We investi gate existing methods in the context of large, pre-trained models and evaluate t heir performance on a variety of text and image classification tasks, including a large-scale study using a novel data set of 15 diverse NLP tasks. Across all s ettings, we observe that generic pre-training implicitly alleviates the effects of catastrophic forgetting when learning multiple tasks sequentially compared to randomly initialized models. We then further investigate why pre-training allev iates forgetting in this setting. We study this phenomenon by analyzing the loss landscape, finding that pre-trained weights appear to ease forgetting by leadin g to wider minima. Based on this insight, we propose jointly optimizing for curr ent task loss and loss basin sharpness to explicitly encourage wider basins duri ng sequential fine-tuning."
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