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 out of Birmingham, United K ingdom, by NewsRx editors, research stated, "Assessment of tunnelling-induced bu ilding damage is a complex Soil-Structure Interaction (SSI) probelm, influenced by numerous geometric and material parameters of both the soil and structures, a nd is characterised by strong non-linear behaviour. Currently, there is a trend towards developing data-driven models using Machine Learning (ML) to capture thi s complex behaviour." Our news journalists obtained a quote from the research from the University of B irmingham, "Given the scarcity of real data, which typically comes from specific case studies, many researchers have turned to creating extensive synthetic data sets via sophisticated and validated numerical models like Finite Element Method (FEM). However, the development of these datasets and the training of advanced ML algorithms present significant challenges. poses significant challenges. Reli ance solely on parameter domains and ranges derived from case studies can lead t o imbalanced data distributions and subsequently poor performance of models in l ess populated regions. In this paper, we introduce a strategy for designing opti mal high-confidence datasets through an iterative procedure. This process begins with a systematic literature review to determine the importance of parameters, their ranges, and dependencies as they pertain to building damage induced by SSI . Starting with several hundred FEM simulations, we generate an initial dataset and assess its quality and impact through Sensitivity Analysis (SA) studies, sta tistical modelling, and re-sampling in statistically significant regions. This e valuation allows us to refine the model's input space, seeking scenarios that mi tigate output distribution imbalances. The procedure is repeated until the datas ets achieve a satisfactory balance for training metamodels, minimising bias effe ctively. Our findings highlight the success of this approach in identifying an o ptimal and feasible input space that significantly reduces imbalanced distributi ons of output features."
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 Dalian, People's Republic of China, by NewsRx journalists, research stated, "The major challenge faced is the defin ition of optimal process variables for rich quality of fabricated parts in laser -based directed energy deposition (DED-LB) processes. However, predicting the op timal process scheme using machine learning models is still challenging owing to the need for a large amount of training experimental data with high costs in DE D-LB." Financial supporters for this research include National Key R & D Program of China, National Natural Science Foundation of China (NSFC), University -Industry Collaborative Education Program of China, Liaoning Provincial Natura l Science Foundation of China, Fundamental Research Funds for the Central Univer sities.
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 artific ial intelligence. According to news originating from Nicosia, Turkey, by NewsRx correspondents, research stated, "The rapid growth in the use of AI techniques, mainly machine learning (ML), is revolutionizing different industries by signifi cantly enhancing decision-making processes through data-driven insights." The news correspondents obtained a quote from the research from Near East Univer sity: "This study investigates the influence of using ML, particularly supervise d and unsupervised learning, on rational decision-making (RDM) within Jordanian e-government, focusing on the mediating role of trust. By analyzing the experien ces of middle-level management within e-government in Jordan, the findings under score that ML positively impacts the rational decision-making process in e-gover nment. It enables more efficient and effective data gathering, improves the accu racy of data analysis, enhances the speed and accuracy of evaluating decision al ternatives, and improves the assessment of potential risks. Additionally, this s tudy reveals that trust plays a critical role in determining the effectiveness o f ML adoption for decision-making, acting as a pivotal mediator that can either facilitate or impede the integration of these technologies. This study provides empirical evidence of how trust not only enhances the utilization of ML but also amplifies its positive impact on governance."
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 originating from Nanj ing, People's Republic of China, by NewsRx correspondents, research stated, "Dro ught has become a major climate threat affecting the growth and yield of agricul tural and forestry crops. Rapid evaluation of drought tolerance, response, and r ecovery plays an important role in the cultivation and management of forestry se edlings." Funders for this research include National Key Research & Developm ent Program of China, National Natural Science Foundation of China (NSFC), Jiang su Province Agricultural Science and Technology Independent Innovation Funds Pro ject, Key Research and Development Program of Jiangsu Province, The 333 Project of Jiangsu Province.
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 in Suzhou, People's Republic of China, by NewsRx journalists, research stated, "Reinforcement learning holds pr omise in enabling robotic tasks as it can learn optimal policies via trial and e rror. However, the practical deployment of reinforcement learning usually requir es human intervention to provide episodic resets when a failure occurs." Financial support for this research came from National Natural Science Foundatio n of China (NSFC). The news reporters obtained a quote from the research from the Suzhou University of Science and Technology, "Since manual resets are generally unavailable in au tonomous robots, we propose a reset-free reinforcement learning algorithm based on multi-state recovery and failure prevention to avoid failureinduced resets. The multi-state recovery provides robots with the capability of recovering from failures by self-correcting its behavior in the problematic state and, more impo rtantly, deciding which previous state is the best to return to for efficient re -learning. The failure prevention reduces potential failures by predicting and e xcluding possible unsafe actions in specific states."
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 from Suzhou, People's Republic of Chi na, by NewsRx journalists, research stated, "Wheeled bipedal robots (WBRs) have the capability to execute agile and versatile locomotion tasks. This article foc uses on improving the dynamic performance of WBRs through innovations in both ha rdware and software development." Funders for this research include Jiangsu Science and Technology Program, Nation al Natural Science Foundation of China (NSFC). The news correspondents obtained a quote from the research from Xi'an Jiaotong L iverpool University, "Inspired by the human barbell squat, a bionic mechanical d esign is proposed and implemented as shown in Fig. 1, where the torque load coul d be evenly distributed onto the hip and knee joints to reduce peak torque and a lleviate potential overheat, improving the effectiveness of joint motor torques while maintaining a relatively large workspace and maximizing the load capacity. Meanwhile, a novel modelbased controller is devised, synthesizing height-varia ble wheeled linear inverted pendulum (HV-wLIP) model, control Lyapunov function (CLF) and whole-body dynamics for theoretically guaranteed stability and efficie nt computation. The HV-wLIP surpasses other alternatives in terms of agility by providing a more accurate approximation of wheeled-bipedal locomotion and provid e theoretical base for WBR controller design."
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 from Guangzhou, People's Re public of China, by NewsRx journalists, research stated, "Icing on transmission lines may cause damage to tower components and even lead to structural failure. Aiming at the lack of research on predicting mechanical characteristic parameter s of weak components of transmission towers, and the cumbersome steps of buildin g a finite element model (FEM), the study of prediction for mechanical character istic parameters of weak components of towers based on a finite element simulati on and machine learning is proposed."
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 originating from Wenzhou, People's Republic of China, by NewsRx correspondents, research stated, "Nowadays more and more intell igent robots are being used in scenarios that closely interact with humans, such as collaboration, healthcare, services, etc. This requires robots to have the a bility to interact with human safely." Financial supporters for this research include China Postdoctoral Science Founda tion, Natural Science Foundation of Zhejiang Province, Wenzhou Major Technology Innovation Project, Wenzhou Industrial Science and Technology Project, Wenzhou A ssociation for Science and Technology.
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 Artific ial Intelligence. According to news reporting from Coral Gables, Florida, by New sRx journalists, research stated, "The applications of artificial intelligence ( AI) and machine learning (ML) approaches are rising in formula optimization, ing redient selection, performance prediction, and structure- properties analysis in formulated product development for the cosmetic industry. The present review ai ms to give a critical discussion regarding how AI and ML assist in the developme nt of key component materials used in cosmetics and formulated products includin g surfactants, polymers, fragrances, preservatives, and hydrogels." The news correspondents obtained a quote from the research from the University o f Miami, "Hydrogels are reviewed here as a promising candidate to open a new fro ntier for the future cosmetics and personal care product industry, due to their excellent biocompatibility, excellent drug-delivering ability, and high water co ntent. We also discuss the use of ML for formula optimization and hazardous ingr edient detection such as sensitizing and allergic components."
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 originating from Amsterdam, N etherlands, by NewsRx correspondents, research stated, "For the provision of opt imal care in a suicide prevention helpline, it is important to know what contrib utes to positive or negative effects on help seekers. Helplines can often be con tacted through text-based chat services, which produce large amounts of text dat a for use in large-scale analysis." Our news editors obtained a quote from the research from Research Department, "W e trained a machine learning classification model to predict chat outcomes based on the content of the chat conversations in suicide helplines and identified th e counsellor utterances that had the most impact on its outputs. From August 202 1 until January 2023, help seekers (N=6903) scored themselves on factors known t o be associated with suicidality (eg, hopelessness, feeling entrapped, will to l ive) before and after a chat conversation with the suicide prevention helpline i n the Netherlands (113 Suicide Prevention). Machine learning text analysis was u sed to predict help seeker scores on these factors. Using 2 approaches for inter preting machine learning models, we identified text messages from helpers in a c hat that contributed the most to the prediction of the model. According to the m achine learning model, helpers' positive affirmations and expressing involvement contributed to improved scores of the help seekers. Use of macros and ending th e chat prematurely due to the help seeker being in an unsafe situation had negat ive effects on help seekers. This study reveals insights for improving helpline chats, emphasizing the value of an evocative style with questions, positive affi rmations, and practical advice."
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