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 Shaanxi, People's Repu blic of China, by NewsRx editors, research stated, "Oxygen reduction reaction (O RR) and oxygen evolution reaction (OER) are essential for the development of exc ellent bifunctional electrocatalysts, which are key functions in clean energy pr oduction. The emphasis of this study lies in the rapid design and investigation of 153 MN-graphene (Gra)/ MXene (MNO) electrocatalysts for ORR/OER catalytic act ivity using machine learning (ML) and density functional theory (DFT)." Our news journalists obtained a quote from the research from the Shaanxi Univers ity of Technology, "The DFT results indicated that CoN-Gra/TiNO had both good OR R (0.37 V) and OER (0.30 V) overpotentials, while TiN-Gra/MNO and MN-Gra/CrNO ha d high overpotentials. Our research further indicated orbital spin polarization and d-band centers far from the Fermi energy level, affecting the adsorption ene rgy of oxygen-containing intermediates and thus reducing the catalytic activity. The ML results showed that the gradient boosting regression (GBR) model success fully predicted the overpotentials of the monofunctional catalysts RhN-Gra/TiNO (ORR, 0.39 V) and RuN-Gra/WNO (OER, 0.45 V) as well as the overpotentials of the bifunctional catalyst RuN-Gra/WNO (ORR, 0.39 V; OER, 0.45 V)."
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 originating from Ho Chi Minh City, Vi etnam, by NewsRx correspondents, research stated, "The uplift capacity of pipeli ne systems in geotechnical engineering is influenced by internal loading and ext ernal factors, making it a significant consideration in pipeline design problems ." The news journalists obtained a quote from the research from Ho Chi Minh City Un iversity of Technology: "Previous research has conducted experimental tests and numerical solutions to investigate the relationship between force and displaceme nt or the resistance of pipelines in numerous soil media. This paper proposes a machine-learning regression technique to predict the uplift capacity of buried p ipelines in anisotropic clays with parametric analysis. Specifically, the Multiv ariate Adaptive Regression Spline (MARS) is employed to establish the relationsh ip between input parameters, namely the depth ratio (H/D), anisotropic strength ratio (re), load inclination (b), overburden factor (gH/Suc), adhesion factor (a ), and the output uplift resistance (N) obtained from the finite element limit a nalysis (FELA), utilizing the AUS material model integrated with the OptumG2 com mercial program. Furthermore, the sensitivity analysis outcome shows the embedde d depth ratio is the most critical parameter, followed by the anisotropic streng th ratio, overburden factor, load inclination, and adhesion factor. Additionally , the shear velocity field contours show that when the depth ratio and the load inclination increase, the dissipation of shear changes."
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 originating from Ilmen au, Germany, by NewsRx correspondents, research stated, "Artificial intelligence (AI) has become a part of the mainstream public discourse beyond expert communi ties about its risks, benefits, and need for regulation. In particular, since 20 14, the news media have intensified their coverage of this emerging technology a nd its potential impact on most domains of society." Our news editors obtained a quote from the research from Technische Universitat Ilmenau, "Although many studies have analyzed traditional media coverage of AI, analyses of social media, especially videosharing platforms, are rare. In addit ion, research from a risk communication perspective remains scarce, despite the widely recognized potential threats to society from many AI applications. This s tudy aims to detect recurring patterns of societal threat/efficacy in YouTube vi deos, analyze their main sources, and compare detected frames in terms of reach and response. Using a theoretical framework combining framing and risk communica tion, the study analyzed the societal threat/efficacy attributed to AI in easily accessible YouTube videos published in a year when public attention to AI tempo rarily peaked (2018). Four dominant AI frames were identified: the balanced fram e, the high-efficacy frame, the high-threat frame, and the no-threat frame. The balanced and no-threat frames were the most prevalent, with predominantly positi ve and neutral AI narratives that neither adequately address the risks nor the n ecessary societal response from a normative risk communication perspective. The results revealed the specific risks and benefits of AI that are most frequently addressed. Video views and user engagement with AI videos were analyzed." According to the news editors, the research concluded: "Recommendations for effe ctive AI risk communication and implications for risk governance were derived fr om the results."
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 - Artif icial Intelligence are presented in a new report. According to news reporting or iginating from Valencia, Spain, by NewsRx correspondents, research stated, "The proposed research introduces an innovative Virtual Reality (VR) and Large Langua ge Model (LLM) architecture to enhance the learning process across diverse educa tional contexts, ranging from school to industrial settings. Leveraging the capa bilities of LLMs and Retrieval -Augmented Generation (RAG), the architecture cen ters around an immersive VR application." Our news editors obtained a quote from the research from the Polytechnic Univers ity of Valencia, "This application empowers students of all backgrounds to inter actively engage with their environment by posing questions and receiving informa tive responses in text format and with visual hints in VR, thereby fostering a d ynamic learning experience. LLMs with RAG act as the backbones of this architect ure, facilitating the integration of private or domain -specific data into the l earning process. By seamlessly connecting various data sources through data conn ectors, RAG overcomes the challenge of disparate and siloed information reposito ries, including APIs, PDFs, SQL databases, and more. The data indexes provided b y RAG solutions further streamline this process by structuring the ingested data into formats optimized for consumption by LLMs. An empirical study was conducte d to evaluate the effectiveness of this VR and LLM architecture. Twenty particip ants, divided into Experimental and Control groups, were selected to assess the impact on their learning process. The Experimental group utilized the immersive VR application, which allowed interactive engagement with the educational enviro nment, while the Control group followed traditional learning methods. The study revealed significant improvements in learning outcomes for the Experimental grou p, demonstrating the potential of integrating VR and LLMs in enhancing comprehen sion and engagement in learning contexts."
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 Beijing, Peop le's Republic of China, by NewsRx editors, research stated, "Graph contrastive l earning (GCL) has emerged as a promising paradigm for learning graph representat ions." Financial supporters for this research include National Natural Science Foundati on of China; National Key Research And Development Program of China. Our news editors obtained a quote from the research from Beijing University of P osts and Telecommunications: "Recently, the idea of hard negatives is introduced to GCL, which can provide more challenging self-supervised objectives and allev iate over-fitting issues. These methods use different graphs in the same mini-ba tch as negative examples, and assign larger weights to true hard negative ones. However, the influence of such weighting strategies is limited in practice, sinc e a small mini-batch may not contain any challenging enough negative examples. I n this paper, we aim to offer a more flexible solution to affect the hardness of negatives by directly manipulating the representations of negatives. By assumin g that (1) good negative representations should not deviate far from the represe ntations of real graph samples, and (2) the computation process of graph encoder may introduce biases to graph representations, we first design a negative repre sentation generator (NRG) which (1) employs real graphs as prototypes to perturb , and (2) introduces parameterized perturbations through the feed-forward comput ation of the graph encoder to match the biases. Then we design a generation loss to train the parameters in NRG and adaptively generate negative representations for more challenging contrastive objectives."
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 Machine Learning is now available. According to news reporting from Beijing, People's Republic of China , by NewsRx journalists, research stated, "Durability is one of the main reasons limiting the large-scale application of fuel cells. Accurate prediction of stat e of health can help improve fuel cells safety and lifetime." Funders for this research include National Natural Science Foundation of China ( NSFC), Beijing Municipal Science & Technology Commission, Postdoct oral Research Fund Project of China, Opening Foundation of Key Laboratory of Adv anced Manufacture Technology for Automobile Parts, Ministry of Education, Postdo ctor Research Foundation of Shunde Graduate innovation School of University of S cience and Tech- nology Beijing, Fundamental Research Funds for the Central Univ ersities.
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 out of Montreal, Canada, by NewsRx editor s, research stated, "Wave runup is a critical factor that affects coastal floodi ng, shoreline changes, and the damage to coastal structures. Climate change is a lso expected to amplify the impact of wave runup on coastal areas." Financial support for this research came from Natural Sciences and Engineering R esearch Council of Canada (NSERC). Our news journalists obtained a quote from the research from the University of Q uebec, "Therefore, fast and accurate wave runup estimation is essential for effe ctive coastal engineering design and management. However, predicting the time-de pendent wave runup is challenging due to the intrinsic nonlinearities and nonsta tionarity of the process, even with the use of the most advanced machine learnin g techniques. In this study, a physics-informed machine learning-based approach is proposed to efficiently and accurately simulate time-series wave runup. The m ethodology combines the computational efficiency of the Surfbeat (XBSB) mode wit h the accuracy of the nonhydrostatic (XBNH) mode of the XBeach model. Specifical ly, a conditional generative adversarial network (cGAN) is used to map the image representation of wave runup from XBSB to the corresponding image from XBNH. Th ese images are generated by first converting wave runup signals into timefrequen cy scalograms and then transforming them into image representations. The cGAN mo del achieves improved performance in image-to-image mapping tasks by incorporati ng physicsbased knowledge from XBSB. After training the model, the high-fidelit y XBNH-based scalograms can be predicted, which are then used to reconstruct the time-series wave runup using the inverse wavelet transform."
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 the Civil Engineering Department by NewsRx editors, the research stated, "This research focuses on a c omprehensive comparative analysis of shear strength prediction in slab-column co nnections, integrating machine learning, design codes, and finite element analys is (FEA)." Our news journalists obtained a quote from the research from Civil Engineering D epartment: "The existing empirical models lack the influencing parameters that d ecrease their prediction accuracy. In this paper, current design codes of Americ an Concrete Institute 318-19 (ACI 318-19) and Eurocode 2 (EC2), as well as innov ative approaches like the compressive force path method and machine learning mod els are employed to predict the punching shear strength using a comprehensive da tabase of 610 samples. The database consists of seven key parameters including s lab depth (ds), column dimension (cs), shear span ratio (av/d), yield strength o f longitudinal steel (fy), longitudinal reinforcement ratio (rl), ultimate load- carrying capacity (Vu), and concrete compressive strength (fc). Compared with th e design codes and other machine learning models, the particle swarm optimizatio n-based feedforward neural network (PSOFNN) performed the best predictions. PSOF NN predicted the punching shear of flat slab with maximum accuracy with R2 value of 99.37% and least MSE and MAE values of 0.0275% a nd 1.214%, respectively. The findings of the study are validated th rough FEA of slabs to confirm experimental results and machine learning predicti ons that showed excellent agreement with PSOFNN predictions."
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 originating from Vienna, Austria, by NewsRx co rrespondents, research stated, "As social robots are increasingly designed with sophisticated simulations of human skills, it becomes essential to understand bo undary conditions of people's engagement of them as mindful actors. The present paper reports a comprehensive secondary analysis of six studies (total N = 967) on the relationship between mind perception (evaluation of mental capacities) an d mind ascription (explicit assignment of a mind-having status) when people cons ider a humanoid robot in different scenarios." Financial support for this research came from Air Force Office of Scientific Res earch (AFOSR). Our news journalists obtained a quote from the research from the University of V ienna, "Results indicate there is a context-independent, moderate link between p erceptions of affective mental capacity (i.e., ability to feel emotion) and expl icit mind ascription. We further found hints for a weak relationship between per ceptions of reality-interaction capacity (i.e., sensory and agentic abilities) a nd decisions to ascribe mind that may need larger samples in order to be validat ed. Perceptions of social-moral capacities (i.e., evaluations of people and goal s) were not a significant predictor of mind ascription."
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 Assam, India, by NewsR x correspondents, research stated, "Rainfall forecasting is pivotal in improving the lead time for issuing flood warnings and flood management." Funders for this research include Prime Minister's Research Fellowship, Ministry of Education. The news correspondents obtained a quote from the research from Indian Institute of Technology Guwahati: "Machine learning (ML) models are popular as they can e ffectively manage extensive data and non-stationarity of the data series with im proved performance and cost-effective solutions. However, more studies are requi red to understand the dynamic characteristics of rainfall. This study proposes a hybrid model and demonstrates its efficiency in improving the daily rainfall fo recast. Singular spectrum analysis (SSA) was used as a data pre-processing techn ique (successfully removing and identifying the nature of noise) and coupled wit h ML models (artificial neural network (ANN) and support vector machine (SVM)) i mproving daily scale forecast. Since the current response of the hydrological sy stem depends on previous responses, rainfall at the next time step was derived w ith the previous 2, 3, 5 and 7 days of rainfall. Study shows that the first eige n vector derived through SSA is the trend component which has a maximum contribu tion of 18.75%, suggesting it can explain 18.75% of t he given rainfall series."
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