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 originating from Beijing, People’s Republic of China, by NewsRx correspondents, research stated, “Carbon phosphide is a newly discovered two-dimensional semiconductor material which wrinkles and has a significant car rier mobility. Due to lack an accurate force field, the use of molecular dynamic s to study its phonon-dominated thermal conductivity which lead to inaccurate re sults.” Financial supporters for this research include National Key Research and Develop ment Program of China, National Natural Science Foundation of China (NSFC), Rese arch Fund of Bohai University.
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 cyborg and bionic syste ms is now available. According to news originating from Beijing, People’s Republ ic of China, by NewsRx editors, the research stated, “Anthropomorphic hand manip ulation is a quintessential example of embodied intelligence in robotics, presen ting a notable challenge due to its high degrees of freedom and complex inter-jo int coupling.” Financial supporters for this research include National Nature Science Foundatio n of China; State Key Laboratory of Drug Research, Chinese Academy of Sciences. Our news editors obtained a quote from the research from Chinese Academy of Scie nces: “Though recent advancements in reinforcement learning (RL) have led to sub stantial progress in this field, existing methods often overlook the detailed st ructural properties of anthropomorphic hands. To address this, we propose a nove l deep RL approach, Bionic-Constrained Diffusion Policy (Bio-CDP), which integra tes knowledge of human hand control with a powerful diffusion policy representat ion. Our bionic constraint modifies the action space of anthropomorphic hand con trol, while the diffusion policy enhances the expressibility of the policy in hi gh-dimensional continuous control tasks.”By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – A new study on cyborg and bionic syste ms is now available. According to news originating from Beijing, People’s Republ ic of China, by NewsRx editors, the research stated, “Anthropomorphic hand manip ulation is a quintessential example of embodied intelligence in robotics, presen ting a notable challenge due to its high degrees of freedom and complex inter-jo int coupling.” Financial supporters for this research include National Nature Science Foundatio n of China; State Key Laboratory of Drug Research, Chinese Academy of Sciences. Our news editors obtained a quote from the research from Chinese Academy of Scie nces: “Though recent advancements in reinforcement learning (RL) have led to sub stantial progress in this field, existing methods often overlook the detailed st ructural properties of anthropomorphic hands. To address this, we propose a nove l deep RL approach, Bionic-Constrained Diffusion Policy (Bio-CDP), which integra tes knowledge of human hand control with a powerful diffusion policy representat ion. Our bionic constraint modifies the action space of anthropomorphic hand con trol, while the diffusion policy enhances the expressibility of the policy in hi gh-dimensional continuous control tasks.”
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Dentistry - Dental Imp lants is the subject of a report. According to news reporting from Guangdong, Pe ople’s Republic of China, by NewsRx journalists, research stated, “To present a novel drilling protocol of trephine osteotomy technique for autologous bone graf ting with simultaneous implant placement using an autonomous robotic system. The novel protocol consists of 1) preoperative procedures: marker fabrication and f ixation, data acquisition, and preoperative planning; 2) intraoperative procedur es: registration and calibration, and osteotomy and implant placement performed by an autonomous dental implant robot; 3) postoperative procedures: CBCT acquisi tion and accuracy assessment.” The news correspondents obtained a quote from the research from Guangzhou Medica l University, “The protocol was an effective method for implant osteotomy, with no reported intraoperative complications. The implant surgery was successfully c ompleted, and autogenous bone was obtained. Meanwhile, the accuracy of implant p lacement was clinically acceptable, with minor deviations. Trephination-based ro botic surgery can be successfully implemented in implant osteotomy, which might replace freehand implant surgery and conventional drilling protocol. However, fu rther clinical studies are necessary.”
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 Toulouse, France, by NewsRx journalist s, research stated, “This paper presents a concurrent optimization approach for the design and motion of a quadruped in order to achieve energy-efficient cyclic behaviors. Computational techniques are applied to improve the development of a novel quadruped prototype.” Financial supporters for this research include French government as part of the ROBOTEX 2.0 program,Agence Nationale de la Recherche (ANR), EU through the AGIM US project, German Aerospace Centre (DLR), Federal Ministry of Education & Research (BMBF). The news correspondents obtained a quote from the research from the University o f Toulouse, “The scale of the robot and its actuators are optimized for energy e fficiency considering the complete actuator model including friction, torque, an d bandwidth limitations. This method and the optimal bounding trajectories are t ested on the first (non-optimized) prototype design iteration showing that our f ormulation produces a trajectory that (i) can be easily replayed on the real rob ot and (ii) reduces the power consumption w.r.t. hand-tuned motion heuristics. P ower consumption is then optimized for several periodic tasks with co-design. Ou r results include, but are not limited to, a bounding and backflip task. It appe ars that, for jumping forward, robots with longer thighs perform better, while, for backflips, longer shanks are better suited. To explore the tradeoff between these different designs, a Pareto set is constructed to guide the next iteration of the prototype.”
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 new report. According to news reporting originating from Heilongjiang, Pe ople’s Republic of China, by NewsRx correspondents, research stated, “In recent years, exoskeleton robot technology has developed rapidly. Exoskeleton robots th at can be worn on a human body and provide additional strength, speed or other a bilities.” Our news editors obtained a quote from the research from Harbin Engineering Univ ersity: “Exoskeleton robots have a wide range of applications, such as medical r ehabilitation, logistics and disaster relief and other fields. The study goal is to propose a lower limb assistive exoskeleton robot to provide extra power for wearers. The mechanical structure of the exoskeleton robot was designed by using bionics principle to imitate human body shape, so as to satisfy the coordinatio n of man-machine movement and the comfort of wearing. Then a gait prediction met hod based on neural network was designed. In addition, a control strategy accord ing to iterative learning control was designed. The experiment results showed th at the proposed exoskeleton robot can produce effective assistance and reduce th e wearer’s muscle force output.”
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 originating from Beijing, People’s Republic of China, by NewsRx correspondents, research stated, “The global effort toward dec arbonization has intensified the drive for low- carbon fuels. Green hydrogen, ha rnessed from renewable sources such as solar, wind, and hydropower, is emerging as a clean substitute.” Financial supporters for this research include National Natural Science Foundati on of China (NSFC), CNPC Innovation Fund, China University of Petroleum, Beijing , Science and Technology Leading Talents Team Funds for the Central Universities for the Frontiers Science Center for Deep-time Digital Earth, China University of Geosciences (Beijing) (Fundamental Research Funds for the Central Universitie s).Our news journalists obtained a quote from the research from the China Universit y of Petroleum, “Challenges due to the variable needs and instable green hydroge n production highlight the necessity for secure and large - scale storage soluti ons. Among the geological formations, deep saline aquifers are noteworthy due to their abundant capacity and ease of access. Addressing technical hurdles relate d to low working gas recovery rates and excessive water production requires well - designed structures and optimized cushion gas volume. A notable contribution o f this study is the development of a multiobjective optimization (MOO) protocol using a Kalman filter - based approach for early stopping. This method maintains solution accuracy while employing the MOO protocol to design the horizontal wel lbore length and cushion gas volume in an aquifer hydrogen storage project and a ccounting for multiple techno- economic goals. Optimization outcomes indicate th at the proposed multiobjective particle swarm (MOPSO) protocol effectively ident ifies the Pareto optimal sets (POSs) in both two- and three- objective scenarios , requiring fewer iterations. Results from the two- objective optimization study , considering working gas recovery efficacy and project cost, highlight that ext ending the horizontal wellbore improves hydrogen productivity but may lead to un expected fluid extraction. The three- objective optimized hydrogen storage desig n achieves a remarkable 94.36% working gas recovery efficacy and a 59.59% reduction in water extraction.”
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 reporting from Zagreb, Croatia, by NewsRx journali sts, research stated, “This paper presents a study focused on developing robust algorithms for cover factor and porosity calculation through digital image analy sis.” Funders for this research include Croatian Science Foundation; University of Zag reb. The news journalists obtained a quote from the research from University of Zagre b Faculty of Textile Technology: “Computational models based on machine learning for efficient cover factor prediction based on fabric parameters have also been developed. Five algorithms were devised and implemented in MATLAB: the single t hreshold algorithm (ST); multiple linear threshold algorithms, ML-1 and ML-2; an d algorithms with multiple thresholds obtained by the Otzu method, MT-1 and MT-2 . These algorithms were applied to knitted fabrics used for football, swimming, and leisure. Algorithms ML-1 and MT-1, employing multiple thresholds, outperform ed the single threshold algorithm. The ML-1 variant yielded the highest average porosity value at 95.24%, indicating the importance of adaptable th resholding in image analysis. Comparative analysis revealed that algorithm varia nts ML-2 and MT-2 obtain lower cover factors compared to ML-1 and MT-1 but can d etect potential void areas in fabrics with higher reliability.”
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Heart Disorders and Di seases - Heart Failure is the subject of a report. According to news originating from Chengdu, People’s Republic of China, by NewsRx correspondents, research st ated, “There is a lack of tools for accurately identifying the risk of readmissi on for heart failure in elderly patients with arrhythmia. The aim of this study was to establish and compare the performance of the LACE [len gth of stay (‘L’), acute (emergent) admission (‘A’), Charlson comorbidity index (‘C’) and visits to the emergency department during the previous 6 months (‘E’)] index and machine learning in predicting 1 year readmission for heart failure in elderly patients with arrhythmia.” Our news journalists obtained a quote from the research from Chengdu Second Peop le’s Hospital, “Elderly patients with arrhythmia who were hospitalized at Sichua n Provincial People’s Hospital between 1 June 2018 and 31 May 2020 were enrolled . The LACE index was calculated for each patient, and the area under the receive r operating characteristic curve (AUROC) was calculated. Six machine learning al gorithms, combined with three variable selection methods and clinically relevant features available at the time of hospital discharge, were used to develop mach ine learning models. AUROC and area under the precision-recall curve (AUPRC) wer e used to assess discrimination. Shapley additive explanations (SHAP) analysis w as used to explain the contributions of the features. A total of 523 patients we re enrolled, and 108 patients experienced 1 year hospital readmission for heart failure. The AUROC of the LACE index was 0.5886. The complete machine learning m odel had the best predictive performance, with an AUROC of 0.7571 and an AUPRC o f 0.4096. The most important predictors for 1 year readmission were educational level, total triiodothyronine (TT3), aspartate aminotransferase/alanine aminotra nsferase (AST/ALT), number of medications (NOM) and triglyceride (TG) level.”
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 originating from Potsdam, New York, by NewsRx correspondents, research stated, “In in-process quality monitoring for Continuou s Manufacturing (CM) and Critical Quality Attributes (CQA) assessment for Real-t ime Release (RTR) testing, ultrasonic characterization is a critical technology for its direct, non-invasive, rapid, and cost-effective nature. In quality evalu ation with ultrasound, relating a pharmaceutical tablet’s ultrasonic response to its defect state and quality parameters is essential.” Our news journalists obtained a quote from the research from Clarkson University , “However, ultrasonic CQA characterization requires a robust mathematical model , which cannot be obtained with traditional first principles-based modeling appr oaches. Machine Learning (ML) using experimental data is emerging as a critical analytical tool for overcoming such modeling challenges. In this work, a novel D eep Neural Network-based MLdriven Non-Destructive Evaluation (ML-NDE) modeling f ramework is developed, and its effectiveness for extracting and predicting three CQAs, namely defect states, compression force levels, and amounts of disintegra nt, is demonstrated. Using a robotic tablet handling experimental rig, each attr ibute’s distinct waveform dataset was acquired and utilized for training, valida ting, and testing the respective ML models. This study details an advanced algor ithmic quality assessment framework for pharmaceutical CM in which automated RTR testing is expected to be critical in developing cost-effective in-process real -time monitoring systems.”
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 Machine Learn ing have been published. According to news reporting from Davis, California, by NewsRx journalists, research stated, “Irrigation is the most significant consume r of freshwater worldwide. Deciding on the right amount of irrigation is crucial for sustainable water management and food production.” The news correspondents obtained a quote from the research from the University o f California Davis, “The Penman-Monteith (P-M) reference crop evapotranspiration (ETO) is the gold standard in irrigation management and scheduling; however, it s calculation requires measurements from multiple sensors over an extended refer ence grass surface. The cost of land, sensors, maintenance, and water to keep th e grass surface green impedes having a dense network of ETO stations. To solve t his challenge, this research aims to develop an input-limited ETO estimation app roach based on historical weather data and machine learning (ML) algorithms to r elax the need for a reference grass surface. This approach, called ‘SolarET,’ ta kes solar radiation (RS) data as its sole input. RS is the only meteorological d riving factor of ETO that does not rely on the measuring surface. To test the ge neralizability of SolarET, we test its performance over unseen arbitrary locatio ns across California. California is chosen as the case study since it is one of the world’s most hydrologically altered and agriculturally productive regions. I n total, 19,088,736 hourly data samples from 131 automated weather stations have been used in this study. The ML models have been trained over 114 stations and tested over 17 unseen stations, each representing a California climatic zone. Ou r findings point to the superiority of decision tree-based algorithms versus neu ral networks. SolarET works best in irrigation-oriented regions of California (e .g., Central Valley) and is less accurate in coastal and desert zones.”
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