查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Investigators publish new report on intelligent s ystems. According to news originating from Tamil Nadu, India, by NewsRx correspo ndents, research stated, "Clinicians benefit from the use of artificial intellig ence and machine learning techniques applied to health data within health record s, which identify commonalities between them. It enables them to get evidence-ba sed support in recommending shared treatment paths for undiagnosed health record s." Financial supporters for this research include Bill And Melinda Gates Foundation . Our news reporters obtained a quote from the research from SRM Institute of Scie nce and Technology: "The collective inference from these patterns, drawn from an array of health records, further enhances the capacity to mine essential featur es, supporting public health experts in their management of population health co nditions. This paper presents a novel mapping tool model designed to analyze ele ctronic health records and provide healthcare providers with evidence-based deci sion support. The work focuses on the analysis of health records from hospital d atabases, encompassing parameters extracted from routine health checkups. By scr utinizing patterns within examined health records, healthcare providers can seam lessly align with newer health records for diagnosis and treatment recommendatio ns. Core to this approach is the integration of a fuzzy rule-based classifier sy stem within the proposed system. This incorporation facilitates the processing o f health records, extracting pertinent features to augment decision-making with the support of knowledge bases. The model architecture provides flexibility and customizability, enabling easy configuration of the system to accurately map new health records to the examined dataset. Additionally, the model utilizes a spec ially developed distance-measure technique tailored for the proposed fuzzy-based system."
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 Robotics is now availab le. According to news reporting from Qinhuangdao, People's Republic of China, by NewsRx journalists, research stated, "This article investigates the adaptive co mposite learning control problem of robots subject to uncertain dynamics and pre scribed time error constraints. Existing prescribed time error constraint method s only achieve semiglobal results or guarantee system order-dependent convergenc e rate." Financial support for this research came from National Natural Science Foundatio n of China (NSFC). The news correspondents obtained a quote from the research from Yanshan Universi ty, "In this article, by integrating a new prescribed time performance function into a tracking error-based barrier function, a novel prescribed time error cons traint method is proposed with the following appealing features: 1) the constrai nt method is global; 2) the tracking error converges to a compact set with a pro ximate exponential rate, which can be preassigned by the user regardless of syst em order; 3) both settling time and compact set can be preassigned by the user. To handle the uncertain dynamics caused by inaccurate measurement of parameters, a novel fixed-time composite learning robot control (FTCLRC) method is develope d by combining a newly designed nonsingular fixed-time integral terminal sliding mode and the Moore-Penrose pseudoinverse-based composite learning technique. In comparison with existing composite learning robot control methods that can only ensure exponential convergence, or finite-time convergence, which is dependent on the unpredictable excitation strengths and initial system states, the propose d FTCLRC can guarantee that both the tracking error and parameters estimation er ror converge to zero in fixed-time, under a weak IE without singularity issue. I n particular, the convergence time only depends on the user-designed parameters, independent of the system's initial states, and the unpredictable excitation st rengths."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Data detailed on Machine Learning have been presented. According to news reporting out of Seattle, Washington, by News Rx editors, the research stated, "We propose a counterfactual approach to train ‘causality-aware' predictive models that are able to leverage causal information in static anticausal machine learning tasks (i.e., prediction tasks where the o utcome influences the inputs). In applications plagued by confounding, the appro ach can be used to generate predictions that are free from the influence of obse rved confounders." Our news journalists obtained a quote from the research from Sage Bionetworks, " In applications involving observed mediators, the approach can be used to genera te predictions that only capture the direct or the indirect causal influences. M echanistically, we train supervised learners on (counterfactually) simulated inp uts that retain only the associations generated by the causal relations of inter est. We focus on linear models, where analytical results connecting covariances, causal effects, and prediction mean square errors are readily available. Quite importantly, we show that our approach does not require knowledge of the full ca usal graph. It suffices to know which variables represent potential confounders and/or mediators. We investigate the stability of the method with respect to dat aset shifts generated by selection biases and also relax the linearity assumptio n by extending the approach to additive models better able to account for nonlin earities in the data."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-New study results on artificial intelligence have been published. According to news reporting from Roorkee, India, by NewsRx jour nalists, research stated, "The volume of fluid method is widely used for interfa ce capturing in two-phase flows including surface tension." Financial supporters for this research include Science And Engineering Research Board. Our news reporters obtained a quote from the research from Indian Institute of T echnology Roorkee: "Calculation of surface forces requires accurate local interf acial curvature, which, despite receiving considerable attention, remains a chal lenge due to the abrupt variation of volume fraction near the interface. Based o n recent studies showing the potential of data-driven techniques, a machine lear ning (ML) model using a multi-layered artificial neural network is initially dev eloped to predict curvature on structured grids. Known shapes in the form of cir cular interface segments are used to generate a synthetic training dataset consi sting of interfacial curvature and volume fractions. An optimum model configurat ion is carefully obtained, with a larger 5 x 5 input stencil showing increased a ccuracy for test data along with analytical test cases. However, an extension of the model to unstructured grids, required in simulations involving complex geom etries, is non-trivial. To overcome the limitations, a local interface remapping algorithm is proposed where the stencil around a target cell is transformed int o a structured stencil for the generation of the input dataset."
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 Changsha, People's Republic o f China, by NewsRx correspondents, research stated, "By applying a widely used m achine learning technique called natural language processing (NLP) to unstructur ed text from annual reports, we create a new, multi-dimensional measure that cap tures the degree of digitalization capabilities of sensing, seizing, and reconfi guring. We construct a digitalization capabilities dictionary using one of the l atest NLP techniques-the word embedding model-for 36,200 firm-year observations over the period 2010-2021." Financial supporters for this research include National Natural Science Foundati on of China (NSFC), Ministry of Education, China, Natural Science Foundation of Hunan Province, Hunan Provincial Social Science Foundation of China.
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Research findings on artificial intell igence are discussed in a new report. According to news reporting originating fr om the University of British Columbia by NewsRx correspondents, research stated, "The recent emergence of generative artificial intelligence (GenAI) tools, such as ChatGPT, has brought profound changes to higher education. While many studie s have examined the potential use of ChatGPT in teaching and learning, few have explored the opportunities to develop assessments that facilitate the use of mul tiple technological innovations (i.e. traditional AI and GenAI tools)." The news reporters obtained a quote from the research from University of British Columbia: "We conducted qualitative research to address this gap. The assessmen ts of an elective English course in Hong Kong were re-designed to incorporate Ge nAI and other tools. Students were asked to employ and reflect on their use of t hese tools for their writing assessments. We analyzed the written reflections of 74 students and conducted focus group interviews with 28 students. The results suggest that the students possess an acumen for choosing the appropriate online tools for specific purposes. When they can choose freely, they develop skills th at allow them to evaluate and select between traditional AI and GenAI tools when appropriate."
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 originating from Prague, Cz ech Republic, by NewsRx editors, the research stated, "We investigate the predic tability of global expected stock returns across various forecasting horizons us ing machine learning techniques. We find that the predictability of returns decr eases with longer forecasting horizons both in the U.S. and internationally." Our news editors obtained a quote from the research from the Charles University of Prague, "Despite this, we provide evidence that using firm -specific characte ristics can remain profitable even after accounting for transaction costs, espec ially when we consider longer forecasting horizons. Studying the profitability o f long -short portfolios, we highlight a trade-off between higher transaction co sts connected to frequent rebalancing and greater returns on shorter horizons. I ncreasing the forecasting horizon while matching the rebalancing period increase s risk -adjusted returns after transaction costs for the U.S. We combine predict ions of expected returns at multiple horizons using double -sorting and a turnov er reducing strategy, buy/hold spread."
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 Robotics have been published. According to news originating from Singapore, Singapore, by New sRx correspondents, research stated, "Self-sensing cementitious materials are cr itical to smart structural health monitoring with the piezoresistivity as an ind icator for internal stress and cracks in the structure, which have been frequent ly utilized as functional coatings in engineering practices. To facilitate the a utomatic deployment of self-sensing cementitious materials, the compatibility wi th robotic spraying has been systematically investigated in this study." Financial supporters for this research include National Research Foundation, Sin gapore, CES_SDC Pte Ltd, Chip Eng Seng Corporation Ltd.
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 Ningbo, Peo ple's Republic of China, by NewsRx editors, the research stated, "The thermodyna mic and transport properties of high-temperature chloride molten salt systems ar e of great significance for spent fuel reprocessing in the field of nuclear ener gy engineering." Financial supporters for this research include National Natural Science Foundati on of China. Our news editors obtained a quote from the research from Chinese Academy of Scie nces: "Here, by using machine learning based deep potential (DP) method, we trai n a high-precision force field model for the LiCl-KCl-LiF system. During force f ield training, adding new dataset through multiple iterations improves the accur acy of the force field model and its applicability to more configurations. The c omparison of density functional theory (DFT) and DP results for the test dataset indicates that our trained DP model has the same accuracy as DFT. Then, we comp rehensively investigate the local structure, thermophysical properties, and tran sport properties of the LiCl-KCl and LiCl-KCl-LiF molten salt systems using the trained DP model. The effects of temperature and LiF concentration on the above properties are analyzed."
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 originating from Chongqing, People 's Republic of China, by NewsRx correspondents, research stated, "This research aims to evaluate the compressive strength of FRP-confined columns using machine learning models. By systematically organizing codes and models proposed by vario us researchers, significant indicators influencing compressive strength have bee n identified." Our news correspondents obtained a quote from the research from Chongqing Jiaoto ng University: "A comprehensive database comprising 366 samples, including both CFRP and GFRP, has been assembled. Based on this database, a machine learning mo del was developed to accurately predict compressive strength. A thorough evaluat ion was conducted, comparing models proposed by codes and researchers. Additiona lly, a detailed parameter analysis was performed using the XGBoost model. The fi ndings highlight the importance of both code-based and researcher-proposed model s in enhancing our understanding of compressive strength. However, certain model s show tendencies towards conservative or overestimated predictions, indicating the need for further accuracy enhancement."
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