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 from Wuhan, People’s Republic of China, by NewsRx journalists, research stated, “Separating diffuse horizonta l irradiance (DHI) from ground-based global horizontal irradiance observations i s critical owing to lack of direct DHI observations. Existing models are often s ite-specific and time-bound, thereby limiting their universal applicability.” Financial support for this research came from National Natural Science Foundatio n of China (NSFC).
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 new report. According to news originating from SEGi Univer sity by NewsRx correspondents, research stated, “This study applies an artificia l intelligence (AI) method, informed by the Protection Motivation Theory (PMT), to predict the behavioral intentions of tourists in a healthy town in Yunnan.” Our news correspondents obtained a quote from the research from SEGi University: “This study looks at online search data to guess when a lot of tourists will co me by combining text mining with the SPCA-LSTM model. This model combines season al and trend decomposition using Loess (STL) with Long Short-Term Memory (LSTM) networks. The model is more accurate than traditional forecasting methods and pr ovides a daily average tourist flow estimate of 3,247 with minimal prediction er rors. The average absolute error of 806.4074 and the root mean square error (RMS E) of 959.775 further highlight the model’s performance.”
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 out of Chongqing, Peop le’s Republic of China, by NewsRx editors, research stated, “Many different kind s of emergency events have happened more frequently as a result of climate chang es. It is well recognized that emergency logistics (EL) attempting to distribute resources to people and places in need is paramount important to minimize the c aused devastating impact.” Financial support for this research came from National Social Science Fund of Ch ina. Our news journalists obtained a quote from the research from Chongqing Universit y, “Artificial intelligence (AI), with its excellent learning ability and adapti vity to changing conditions, demonstrates superiority to traditional methods. In this article, to gain a clear picture of research on how to apply AI for EL, we divide the resource distribution into three stages (i.e., resource prediction, resource allocation, and resource transportation), and make use of 4W1H (i.e., w hat, when and where, why, and how) to organize the research issues and opportuni ties in each stage. Furthermore, we identify and discuss the limitations of appl ying AI for EI.”
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 originating in Tianjin, People’s Rep ublic of China, by NewsRx journalists, research stated, “In contemporary compile rs, the determination of the loop unrolling factor is traditionally based on man ually crafted heuristic rules. This approach heavily relies on human intuition, which limits its ability to achieve optimized performance across diverse archite ctures and can sometimes even lead to performance declines.” Financial supporters for this research include National Natural Science Foundati on of China (NSFC), Natural Science Foundation of Tianjin. The news reporters obtained a quote from the research from Tianjin University, “ Additionally, developers face challenges in achieving cross-platform compatibili ty, often necessitating extensive redesign efforts. In response, this study intr oduces a method leveraging the XGBoost algorithm to predict the optimal loop unr olling factor for compiler optimization, thereby aiming to replace human thinkin g with machine learning methods and standardize development processes. Initially , the study gathers data on the loop unrolling factors as determined by profile guided optimization technology, analyzes program-specific loop feature vectors a nd employs cross-validation, including the Pearson correlation coefficient and f eature importance ranking, to construct a dataset. Subsequent use of XGBoost to train this dataset models the decision-making process for selecting the most eff ective loop unrolling factor. The final step involves integrating XGBoost’s trai ned decision tree model into GCC to calculate the optimal loop unrolling factor during actual compilation. Empirical results on the RISC-V platform indicate tha t this new method, when tested against the SPEC CPU 2006 benchmark suite, offers up to 6.18% improvement in performance over the existing heuristi c approach.”
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – New study results on robotics have been published . According to news originating from Shanghai, People’s Republic of China, by Ne wsRx editors, the research stated, “Intelligent mobile robots have been graduall y used in various fields, including logistics, healthcare, service, and maintena nce.” Funders for this research include Shanghai Sailing Program. The news correspondents obtained a quote from the research from Shanghai Polytec hnic University: “Path planning is a crucial aspect of intelligent mobile robot research, which aims to empower robots to create optimal trajectories within com plex and dynamic environments autonomously. This study introduces an improved A* algorithm to address the challenges faced by the preliminary A* pathfinding alg orithm, which include limited efficiency, inadequate robustness, and excessive n ode traversal. Firstly, the node storage structure is optimized using a minimum heap to decrease node traversal time. In addition, the heuristic function is imp roved by adding an adaptive weight function and a turn penalty function. The ori ginal 8-neighbor is expanded to a 16-neighbor within the search strategy, follow ed by the elimination of invalid search neighbor to refine it into a new 8-neigh bor according to the principle of symmetry, thereby enhancing the directionality of the A* algorithm and improving search efficiency. Furthermore, a bidirection al search mechanism is implemented to further reduce search time.”
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 from Geelong, Australia, by N ewsRx journalists, research stated, “The advent of 3D printing has transformed m anufacturing. However, extending the library of materials to improve 3D printing quality remains a challenge.” Financial support for this research came from Australian Research Council. The news correspondents obtained a quote from the research from Deakin Universit y, “Defects can occur when printing parameters like print speed and temperature are chosen incorrectly. These can cause structural or dimensional issues in the final product. This review investigates closed-loop artificial intelligence-augm ented additive manufacturing (AI2AM) technology that integrates AI-based monitor ing, automation, and optimization of printing parameters and processes. AI2AM us es AI to improve defect detection and prevention, improving additive manufacturi ng quality and efficiency. This article explores generic 3D printing processes a nd issues using existing research and developments. Next, it focuses on fused de position modeling (FDM) printers and reviews their parameters and issues. The cu rrent remedies developed for defect detection and monitoring in FDM 3D printers are presented. Then, the article investigates AI-based 3D printing monitoring, c losed-loop feedback systems, and parameter optimization development. Finally, cl osed-loop 3D printing challenges and future directions are discussed. AI-based s ystems detect and correct 3D printing failures, enabling current printers to ope rate within optimal conditions and minimizing the risk of defects or failures, w hich in turn leads to more sustainable manufacturing with minimum waste and exte nding the library of materials. This review delves into artificial intelligence (AI)-augmented additive manufacturing, enhancing defect detection and closed-loo p optimization to boost manufacturing efficiency and quality.”
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 out of Chicago, Illino is, by NewsRx editors, research stated, “Research on the Epic Sepsis System (ESS ) has predominantly focused on technical accuracy, neglecting the user experienc e of healthcare professionals. Understanding these experiences is crucial for th e design of Artificial Intelligence (AI) systems in clinical settings.” Our news journalists obtained a quote from the research from the University of I llinois Chicago, “This study aims to explore the socio-technical dynamics affect ing ESS adoption and use, based on user perceptions and experiences. Resident do ctors and nurses with recent ESS interaction were interviewed using purposive sa mpling until data saturation. A content analysis was conducted using Dedoose sof tware, with codes generated from Sittig and Singh’s and Salwei and Carayon’s fra meworks, supplemented by inductive coding for emerging themes. Interviews with 1 0 healthcare providers revealed mixed but generally positive or neutral percepti ons of the ESS. Key discussion points included its workflow integration and usab ility. Findings were organized into 2 main domains: workflow fit, and usability and utility, highlighting the system’s seamless electronic health record integra tion and identifying design gaps. This study offers insights into clinicians’ ex periences with the ESS, emphasizing the socio-technical factors that influence i ts adoption and effective use. The positive reception was tempered by identified design issues, with clinician perceptions varying by their professional experie nce and frequency of ESS interaction. The findings highlight the need for ongoin g ESS refinement, emphasizing a balance between technological advancement and cl inical practicality.”
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 originating from Gainesville, Fl orida, by NewsRx correspondents, research stated, “Frailty assessment is imperat ive for tailoring healthcare interventions for older adults, but its implementat ion remains challenging due to the effort and time needed. The advances of artif icial intelligence (AI) and natural language processing (NLP) present a novel op portunity to harness real-world data (RWD) including electronic health records, administrative claims, and other routinely collected medical records for frailty assessments.” Our news journalists obtained a quote from the research from the University of F lorida, “We followed the PRISMA-ScR guideline and searched Embase, Web of Scienc e, and PubMed databases for articles that predict frailty using AI through RWD f rom inception until October 2023. We synthesized and analyzed the selected publi cations according to their field of application, methodologies employed, validat ion processes, outcomes achieved, and their respective limitations and strengths . A total of 23 publications were selected from the initial search (N=2067) and bibliography. The approaches to frailty prediction using RWD and AI were categor ized into two groups based on the type of data utilized: 1) AI models using stru ctured data and 2) NLP techniques applied to unstructured clinical notes. We fou nd that AI models achieved moderate to high predictive performance in predicting frailty. However, to demonstrate their clinical utility, these models require f urther validation using external data and a comprehensive assessment of their im pact on patients’ health outcomes. Additionally, the application of NLP in frail ty prediction is still in its early stages. Great potential exists to enhance fr ailty prediction by integrating structured data and clinical notes. The combinat ion of AI and RWD presents significant opportunities for advancing frailty asses sment.”
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – New research on Robotics is the subject of a repo rt. According to news reporting originating in Selangor, Malaysia, by NewsRx jou rnalists, research stated, “When a mobile robot moves in an unknown environment, the emergence of Simultaneous Localization and Mapping (SLAM) technology become s crucial for accurately perceiving its surroundings and determining its positio n in the environment. SLAM technology successfully addresses the issues of low l ocalization accuracy and inadequate real-time performance of traditional mobile robots.” Financial supporters for this research include City University Malaysia, Cardiff Metropolitan University, UK.
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 from Beijing, People’s Republic of Chi na, by NewsRx journalists, research stated, “Anaerobic digestion (AD) is a well- established pathway for treating agricultural organic waste, and machine learnin g has emerged as a novel tool to predict its product performance. In prior resea rch, the majority of studies concentrated on non-time series models for laborato ry-scale fermentation data.” Financial supporters for this research include China Agriculture Research System of MOF and MARA, National Natural Science Foundation of China (NSFC), Agricultu ral Science and Technology Innovation Pro-gram (ASTIP) of China. The news correspondents obtained a quote from the research from the Chinese Acad emy of Agricultural Sciences, “Consequently, the generalization performance of t hese models was significantly constrained, particularly in the context of indust rial-scale biogas projects. Thus, in this study, typical non-time series models (GBR and RF) and time-series models (LSTM, CNN-LSTM, and DA-LSTM) after hyperpar ameter optimization were chosen to accurately predict the characteristics of dig estion products in a biogas project. The ideal GBR model for CH4 content was obt ained, and the R-2 values of the test set and training set were 0.93 (R-MSE=1.11 ) and 0.97 (R-MSE=0.69), respectively. Temperature was the most important parame ter for biogas production according to feature importance and SHAP analysis of t he RF model.”
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