Machine learning-assisted retrosynthesis planning aims to utilize machine learning(ML)algorithms to find synthetic pathways for target *** recent years,with the development of artificial intelligence(AI),especially ML...
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Machine learning-assisted retrosynthesis planning aims to utilize machine learning(ML)algorithms to find synthetic pathways for target *** recent years,with the development of artificial intelligence(AI),especially ML,researchers’interest in ML-assisted retrosynthesis planning has rapidly increased,bringing development and opportunities to the *** this review,we aim to provide a comprehensive understanding of ML-assisted retrosynthesis *** first discuss the formal definition and the objective of retrosynthesis planning,and organize a modular framework which includes four modules:data preparation,data preprocessing,pathway generation and evaluation,and pathway ***,we sequentially review the current status of the first three modules(except pathway verification)in the ML-assisted retrosynthesis planning framework,including ideas,methods,and latest *** that,we specifically discuss large language models in retrosynthesis ***,we summarize the extant challenges that are faced by current ML-assisted retrosynthesis planning research and offer a perspective on future research directions and development.
On-site lithium-ion battery state of health (SoH) estimation is of crucial importance for reliable operations of electric vehicles (EVs). Yet, due to the low-quality of unlabeled real-time field data, diverse operatin...
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Tables,typically two-dimensional and structured to store large amounts of data,are essential in daily activities like database queries,spreadsheet manipulations,Web table question answering,and image table information...
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Tables,typically two-dimensional and structured to store large amounts of data,are essential in daily activities like database queries,spreadsheet manipulations,Web table question answering,and image table information *** these table-centric tasks with Large Language Models(LLMs)or Visual Language Models(VLMs)offers significant public benefits,garnering interest from academia and *** survey provides a comprehensive overview of table-related tasks,examining both user scenarios and technical *** covers traditional tasks like table question answering as well as emerging fields such as spreadsheet manipulation and table data *** summarize the training techniques for LLMs and VLMs tailored for table ***,we discuss prompt engineering,particularly the use of LLM-powered agents,for various tablerelated ***,we highlight several challenges,including diverse user input when serving and slow thinking using chainof-thought.
In this study, a novel Ca2GaTaO6:Sm3+ phosphor was developed using the conventional high-temperature solid-phase method. The phase structure and morphology test results of phosphor indicate that the Ca2GaTaO6 material...
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Feature selection methods rooted in rough sets confront two notable limitations:their high computa-tional complexity and sensitivity to noise,rendering them impractical for managing large-scale and noisy *** primary i...
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Feature selection methods rooted in rough sets confront two notable limitations:their high computa-tional complexity and sensitivity to noise,rendering them impractical for managing large-scale and noisy *** primary issue stems from these methods’undue reliance on all *** overcome these challenges,we introduce the concept of cross-similarity grounded in a robust fuzzy relation and design a rapid and robust feature selection ***,we construct a robust fuzzy relation by introducing a truncation ***,based on this fuzzy relation,we propose the concept of cross-similarity,which emphasizes the sample-to-sample similarity relations that uniquely determine feature importance,rather than considering all such relations *** studying the manifestations and properties of cross-similarity across different fuzzy granularities,we propose a forward greedy feature selection algorithm that leverages cross-similarity as the foundation for information *** algorithm significantly reduces the time complexity from O(m2n2)to O(mn2).Experimental findings reveal that the average runtime of five state-of-the-art comparison algorithms is roughly 3.7 times longer than our algorithm,while our algorithm achieves an average accuracy that surpasses those of the five comparison algorithms by approximately 3.52%.This underscores the effectiveness of our *** paper paves the way for applying feature selection algorithms grounded in fuzzy rough sets to large-scale gene datasets.
Network topology planning is an essential multi-phase process to build and jointly optimize the multi-layer network topologies in wide-area networks (WANs). Most existing practices target single-phase/layer planning, ...
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Accurately estimating the State of Health(SOH)and Remaining Useful Life(RUL)of lithium-ion batteries(LIBs)is crucial for the continuous and stable operation of battery management ***,due to the complex internal chemic...
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Accurately estimating the State of Health(SOH)and Remaining Useful Life(RUL)of lithium-ion batteries(LIBs)is crucial for the continuous and stable operation of battery management ***,due to the complex internal chemical systems of LIBs and the nonlinear degradation of their performance,direct measurement of SOH and RUL is *** address these issues,the Twin Support Vector Machine(TWSVM)method is proposed to predict SOH and ***,the constant current charging time of the lithium battery is extracted as a health indicator(HI),decomposed using Variational Modal Decomposition(VMD),and feature correlations are computed using Importance of Random Forest Features(RF)to maximize the extraction of critical factors influencing battery performance ***,to enhance the global search capability of the Convolution Optimization Algorithm(COA),improvements are made using Good Point Set theory and the Differential Evolution *** Improved Convolution Optimization Algorithm(ICOA)is employed to optimize TWSVM parameters for constructing SOH and RUL prediction ***,the proposed models are validated using NASA and CALCE lithium-ion battery *** results demonstrate that the proposed models achieve an RMSE not exceeding 0.007 and an MAPE not exceeding 0.0082 for SOH and RUL prediction,with a relative error in RUL prediction within the range of[-1.8%,2%].Compared to other models,the proposed model not only exhibits superior fitting capability but also demonstrates robust performance.
Should a firm engage in bundling to boost revenue when consumer's valuations of products are heterogeneous and uncertain? In recent years, technological advances have made it possible for firms to use large amount...
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Represented by evolutionary algorithms and swarm intelligence algorithms, nature-inspired metaheuristics have been successfully applied to recommender systems and amply demonstrated effectiveness, in particular, for m...
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Breast cancer is a serious and high morbidity disease in women,and it is the main cause of cancer death in ***,getting tested and diagnosed early can reduce the risk of *** present,there are clinical examinations,imag...
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Breast cancer is a serious and high morbidity disease in women,and it is the main cause of cancer death in ***,getting tested and diagnosed early can reduce the risk of *** present,there are clinical examinations,imaging screening and biopsies,among which histopathological examination is the gold ***,the process is complicated and time-consuming,and misdiagnosis may *** paper puts forward a classification framework based on deep learning,introducing multi-attention mechanism,selecting kernel convolution instead of ordinary convolution,and using different weights and combinations to pay attention to the accuracy index and growth rate of the *** addition,we also compared the learning rate *** function can fine-tune the learning rate to achieve good performance,using label softening to reduce the loss error caused by model error recognition in the label,and assigning different category weights in the loss function to balance the positive and negative *** used the BreakHis data set to automatically classify histological images into benign and malignant,four categories and eight *** results showed that the accuracy of binary classifications ranged from 98.23%to 98.83%,and that of multiple classifications ranged from 97.89%to 98.11%.
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