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 out of Assam, India, by NewsRx editor s, research stated, “The snake robot can be used to monitor and maintain underwa ter structures and environments. The motion of a snake robot is achieved by late ral undulation which is called the gait pattern of the snake robot.” Financial support for this research came from IITGTIDF.
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 out of Porto, Portugal, by NewsRx editors, research stated, “Artificial intelligence (AI) has emerged as a transformative tool across several specialties, namely gastroenterology, where i t has the potential to optimize both diagnosis and treatment as well as enhance patient care.” The news reporters obtained a quote from the research from Sao Joao University H ospital: “Coloproctology, due to its highly prevalent pathologies and tremendous potential to cause significant mortality and morbidity, has drawn a lot of atte ntion regarding AI applications. In fact, its application has yielded impressive outcomes in various domains, colonoscopy being one prominent example, where it aids in the detection of polyps and early signs of colorectal cancer with high a ccuracy and efficiency. With a less explored path but equivalent promise, AI-pow ered capsule endoscopy ensures accurate and time-efficient video readings, alrea dy detecting a wide spectrum of anomalies. High-resolution anoscopy is an area t hat has been growing in interest in recent years, with efforts being made to int egrate AI. There are other areas, such as functional studies, that are currently in the early stages, but evidence is expected to emerge soon.”
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 out of Evanston, Illinois, by NewsRx editors , research stated, “Operating multi-robot teams of diverse agents is an ongoing challenge for emergency deployments, where inter-agent connectivity is rare and environments are unpredictable. Heterogeneous systems must be capable of adaptin g autonomously while maintaining safety.” Funders for this research include MURI, National Science Foundation (NSF), Offic e of Naval Research. Our news journalists obtained a quote from the research from Northwestern Univer sity, “Here, we develop an algorithm for heterogeneous decentralized multi-robot systems to independently manage safety constraints with provable guarantees for safety and communication in a coverage task. We demonstrate this algorithm in s ettings where up to 100 agents navigate a simulated cluttered environment with s afety constraints that change as agents observe hazards.”
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Current study results on Machine Learning have be en published. According to news reporting out of Rome, Italy, by NewsRx editors, research stated, “Shoulder rehabilitation is considered one of the most effecti ve treatments for restoring functional abilities, reducing shoulder pain, and en abling the leading of an active life, improving mobility, strength, and enduranc e. However, the burdens of travel and time may prevent patients from taking part in such rehabilitation programs.” Funders for this research include Marie Curie Actions, Italian Ministry of Healt h in the framework of Ricerca Finalizzata 2021.
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 originating from Nanning, People’s Republic of China, by NewsRx correspondents, research stated, “This work proposes a nove l grasp detection method, the Efficient Grasp Aware Network (EGA-Net), for robot ic visual grasp detection. Our method obtains semantic information for grasping through feature extraction.” Financial support for this research came from National Natural Science Foundatio n of Guangxi Province. Our news editors obtained a quote from the research from Guangxi University, “It efficiently obtains feature channel weights related to grasping tasks through t he constructed ECA-ResNet module, which can smooth the network’s learning. Meanw hile, we use concatenation to obtain low-level features with rich spatial inform ation. Our method inputs an RGB-D image and outputs the grasp poses and their qu ality score. The EGA-Net is trained and tested on the Cornell and Jacquard datas ets, and we achieve 98.9% and 95.8% accuracy, respec tively. The proposed method only takes 24 ms for real-time performance to proces s an RGB-D image. Moreover, our method achieved better results in the comparison experiment. In the real-world grasp experiments, we use a 6-degree of freedom ( DOF) UR-5 robotic arm to demonstrate its robust grasping of unseen objects in va rious scenes. We also demonstrate that our model can successfully grasp differen t types of objects without any processing in advance.”
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 Robotic s. According to news reporting originating in Guangzhou, People’s Republic of Ch ina, by NewsRx journalists, research stated, “The gripping operation is paramoun t for biped climbing robots (BiCRs), as an improper grip may result in high-rise falls. Automating this operation contributes to expediting grip establishment a nd enhancing grip quality.” Funders for this research include Guangdong Provincial Basic and Applied Basic R esearch Fund- Enterprise Joint Fund (Offshore Wind Power), Research and Developme nt Programs in Key Areas of Guangdong Province, Foshan Science and Technology In novation Team Project.
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 Zarqa, Jordan, by NewsRx editors, research stated, “In the rapidly evolving landscape of the I nternet of Things (IoT), cybersecurity remains a critical challenge due to the d iverse and complex nature of network traffic and the increasing sophistication o f cyber threats.”
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 Little Rock, Arkansas, by NewsRx correspondents, research stated, “Energies of the atomic and molecula r orbitals belonging to one and two atom systems from the fourth and fifth perio ds of the periodic table have been calculated using ab initio quantum mechanical calculations.” The news journalists obtained a quote from the research from University of Arkan sas Little Rock: “The energies of selected occupied and unoccupied orbitals surr ounding the highest occupied and lowest unoccupied orbitals (HOMOs and LUMOs) of each system were selected and used as input for our artificial intelligence (AI ) software. Using the AI software, correlations between orbital parameters and s elected chemical and physical properties of bulk materials composed of these ele ments were established. Using these correlations, the materials’ bulk properties were predicted. The Q2 correlation for the single-atom predictions of first ion ization potential, melting point, and boiling point were 0.3589, 0.4599, and 0.1 798 respectively. The corresponding Q2 correlations using orbital parameters des cribing two-atom systems increased the capability to predict the experimental pr operties to the respective values of 0.8551, 0.8207, and 0.7877.”
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 Tianjin, People’s Repu blic of China, by NewsRx correspondents, research stated, “Nitrate dynamics with in a catchment are critical to the earth’s system process, yet the intricate det ails of its transport and transformation at high resolutions remain elusive. Hyd rological effects on nitrate dynamics in particular have not been thoroughly ass essed previously and this knowledge gap hampers our understanding and effective management of nitrogen cycling in watersheds.” Our news journalists obtained a quote from the research from Tianjin University, “Here, machine learning (ML) models were employed to reconstruct the annual var iation trend in nitrate dynamics and isotopes within a typical karst catchment. Random forest model demonstrates promising potential in predicting nitrate conce ntration and its isotopes, surpassing other ML models (including Long Short-term Memory, Convolutional Neural Network, and Support Vector Machine) in performanc e. The ML-modeled NO-N concentrations, dN-NO, and dO-NO values were in close agr eement with field data (NSE values of 0.95, 0.80, and 0.53, respectively), which are notably challenging to achieve for process models. During the transition fr om dry to wet period, approximately 23.0 % of the annual precipita tion ( 269.1 mm) was identified as the threshold for triggering a rapid response in the wet period. The modeled nitrate isotope values were significantly suppor ted by the field data, suggesting seasonal variations of nitrogen sources, with precipitation as the primary driving force for fertilizer sources.”
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 originating from Athens, Geor gia, by NewsRx correspondents, research stated, “Given the vulnerability of surf ace water to the direct impacts of climate change, the accurate prediction of gr oundwater levels has become increasingly important, particularly for dry regions , offering significant resource management benefits.” Funders for this research include Department of Geology, The University of Georg ia. Our news editors obtained a quote from the research from University of Georgia: “This study presents the first statewide groundwater level anomaly (GWLA) predic tion for Arizona across its two distinct aquifer types-unconsolidated sand and g ravel aquifers and rock aquifers. Machine learning (ML) models were combined wit h empirical Bayesian kriging (EBK) geostatistical interpolation models to predic t monthly GWLAs between January 2010 and December 2019. Model evaluations were b ased on the Nash-Sutcliffe efficiency (NSE) and coefficient of determination (R2 ) metrics.”
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