In the wave of intelligent transformation in the textile printing and dyeing industry,Changzhou Hongda Intelligence Technology Co.,Ltd.stands as a leading enterprise specializing in online inspection equipment and int...In the wave of intelligent transformation in the textile printing and dyeing industry,Changzhou Hongda Intelligence Technology Co.,Ltd.stands as a leading enterprise specializing in online inspection equipment and intelligent digital production lines.The company boasts strong independent innovation capabilities and internationally advanced technologies.With core strengths rooted in a deep understanding and research of smart manufacturing for the industry,Hongda possesses cutting-edge expertise in machine vision,artificial intelligence,digital twin,and automation technologies.It leads the development of intelligent manufacturing in textile printing and dyeing and has become an industry benchmark.展开更多
This study aimed to develop a predictive model utilizing available data to forecast the risk of future shark attacks, making this critical information accessible for everyday public use. Employing a deep learning/neur...This study aimed to develop a predictive model utilizing available data to forecast the risk of future shark attacks, making this critical information accessible for everyday public use. Employing a deep learning/neural network methodology, the system was designed to produce a binary output that is subsequently classified into categories of low, medium, or high risk. A significant challenge encountered during the study was the identification and procurement of appropriate historical and forecasted marine weather data, which is integral to the model’s accuracy. Despite these challenges, the results of the study were startlingly optimistic, showcasing the model’s ability to predict with impressive accuracy. In conclusion, the developed forecasting tool not only offers promise in its immediate application but also sets a robust precedent for the adoption and adaptation of similar predictive systems in various analogous use cases in the marine environment and beyond.展开更多
In a prior practice and policy article published in Healthcare Science,we introduced the deployed application of an artificial intelligence(AI)model to predict longer‐term inpatient readmissions to guide community ca...In a prior practice and policy article published in Healthcare Science,we introduced the deployed application of an artificial intelligence(AI)model to predict longer‐term inpatient readmissions to guide community care interventions for patients with complex conditions in the context of Singapore's Hospital to Home(H2H)program that has been operating since 2017.In this follow on practice and policy article,we further elaborate on Singapore's H2H program and care model,and its supporting AI model for multiple readmission prediction,in the following ways:(1)by providing updates on the AI and supporting information systems,(2)by reporting on customer engagement and related service delivery outcomes including staff‐related time savings and patient benefits in terms of bed days saved,(3)by sharing lessons learned with respect to(i)analytics challenges encountered due to the high degree of heterogeneity and resulting variability of the data set associated with the population of program participants,(ii)balancing competing needs for simpler and stable predictive models versus continuing to further enhance models and add yet more predictive variables,and(iii)the complications of continuing to make model changes when the AI part of the system is highly interlinked with supporting clinical information systems,(4)by highlighting how this H2H effort supported broader Covid‐19 response efforts across Singapore's public healthcare system,and finally(5)by commenting on how the experiences and related capabilities acquired from running this H2H program and related community care model and supporting AI prediction model are expected to contribute to the next wave of Singapore's public healthcare efforts from 2023 onwards.For the convenience of the reader,some content that introduces the H2H program and the multiple readmissions AI prediction model that previously appeared in the prior Healthcare Science publication is repeated at the beginning of this article.展开更多
The advent of the artificial intelligence(AI)age offers substantial potentials for predicting regional gross domestic product(GDP)growth and transportation dynamics.This article presents an in-depth overview of the AI...The advent of the artificial intelligence(AI)age offers substantial potentials for predicting regional gross domestic product(GDP)growth and transportation dynamics.This article presents an in-depth overview of the AI and empirical modeling techniques used in this area,emphasizing the significant possibilities that AI presents and discussing potential obstacles.The use of AI is essential in managing complicated data,allowing for effective analysis of detailed regional economic trends.This capacity will be essential for making economic policies and plans that respond to each region’s specific needs and capabilities.This paper first explores the relationship and impact of different modes of transportation and regional economic growth.Subsequently,various empirical models and methodological frameworks,including the factors employed for studied economic analysis were comprehensively discussed and summarized.In the last part,the discussion focuses on the potential role of AI to revolutionize regional economic research using different AI approaches.This includes its capacity to handle vast and intricate databases,its ability to forecast future patterns using historical and current data,and its assistance in advanced decision making.The present study enhances our awareness of how AI is revolutionizing the field of regional economic growth study,shedding light on both its current application and future possibilities.This study contributes to the advancement of AI predictive models in decision making for predicting regional economic growth across the globe.展开更多
Predicting the material stability is essential for accelerating the discovery of advanced materials in renewable energy, aerospace, and catalysis. Traditional approaches, such as Density Functional Theory (DFT), are a...Predicting the material stability is essential for accelerating the discovery of advanced materials in renewable energy, aerospace, and catalysis. Traditional approaches, such as Density Functional Theory (DFT), are accurate but computationally expensive and unsuitable for high-throughput screening. This study introduces a machine learning (ML) framework trained on high-dimensional data from the Open Quantum Materials Database (OQMD) to predict formation energy, a key stability metric. Among the evaluated models, deep learning outperformed Gradient Boosting Machines and Random Forest, achieving up to 0.88 R2 prediction accuracy. Feature importance analysis identified thermodynamic, electronic, and structural properties as the primary drivers of stability, offering interpretable insights into material behavior. Compared to DFT, the proposed ML framework significantly reduces computational costs, enabling the rapid screening of thousands of compounds. These results highlight ML’s transformative potential in materials discovery, with direct applications in energy storage, semiconductors, and catalysis.展开更多
A recent systematic experimental characterisation of technological thin films,based on elaborated design of experiments as well as probe calibration and correction procedures,allowed for the first time the determinati...A recent systematic experimental characterisation of technological thin films,based on elaborated design of experiments as well as probe calibration and correction procedures,allowed for the first time the determination of nanoscale friction under the concurrent influence of several process parameters,comprising normal forces,sliding velocities,and temperature,thus providing an indication of the intricate correlations induced by their interactions and mutual effects.This created the preconditions to undertake in this work an effort to model friction in the nanometric domain with the goal of overcoming the limitations of currently available models in ascertaining the effects of the physicochemical processes and phenomena involved in nanoscale contacts.Due to the stochastic nature of nanoscale friction and the relatively sparse available experimental data,meta-modelling tools fail,however,at predicting the factual behaviour.Based on the acquired experimental data,data mining,incorporating various state-of-the-art machine learning(ML)numerical regression algorithms,is therefore used.The results of the numerical analyses are assessed on an unseen test dataset via a comparative statistical validation.It is therefore shown that the black box ML methods provide effective predictions of the studied correlations with rather good accuracy levels,but the intrinsic nature of such algorithms prevents their usage in most practical applications.Genetic programming-based artificial intelligence(AI)methods are consequently finally used.Despite the marked complexity of the analysed phenomena and the inherent dispersion of the measurements,the developed AI-based symbolic regression models allow attaining an excellent predictive performance with the respective prediction accuracy,depending on the sample type,between 72%and 91%,allowing also to attain an extremely simple functional description of the multidimensional dependence of nanoscale friction on the studied variable process parameters.An effective tool for nanoscale friction prediction,adaptive control purposes,and further scientific and technological nanotribological analyses is thus obtained.展开更多
Inflammatory bowel disease(IBD)is a chronic inflammatory condition caused by multiple genetic and environmental factors.Numerous genes are implicated in the etiology of IBD,but the diagnosis of IBD is challenging.Here...Inflammatory bowel disease(IBD)is a chronic inflammatory condition caused by multiple genetic and environmental factors.Numerous genes are implicated in the etiology of IBD,but the diagnosis of IBD is challenging.Here,XGBoost,a machine learning prediction model,has been used to distinguish IBD from healthy cases following elaborative feature selection.Using combined unsupervised clustering analysis and the XGBoost feature selection method,we successfully identified a 32-gene signature that can predict IBD occurrence in new cohorts with 0.8651 accuracy.The signature shows enrichment in neutrophil extracellular trap formation and cytokine signaling in the immune system.The probability threshold of the XGBoost-based classification model can be adjusted to fit personalized lifestyle and health status.Therefore,this study reveals potential IBD-related biomarkers that facilitate an effective personalized diagnosis of IBD.展开更多
文摘In the wave of intelligent transformation in the textile printing and dyeing industry,Changzhou Hongda Intelligence Technology Co.,Ltd.stands as a leading enterprise specializing in online inspection equipment and intelligent digital production lines.The company boasts strong independent innovation capabilities and internationally advanced technologies.With core strengths rooted in a deep understanding and research of smart manufacturing for the industry,Hongda possesses cutting-edge expertise in machine vision,artificial intelligence,digital twin,and automation technologies.It leads the development of intelligent manufacturing in textile printing and dyeing and has become an industry benchmark.
文摘This study aimed to develop a predictive model utilizing available data to forecast the risk of future shark attacks, making this critical information accessible for everyday public use. Employing a deep learning/neural network methodology, the system was designed to produce a binary output that is subsequently classified into categories of low, medium, or high risk. A significant challenge encountered during the study was the identification and procurement of appropriate historical and forecasted marine weather data, which is integral to the model’s accuracy. Despite these challenges, the results of the study were startlingly optimistic, showcasing the model’s ability to predict with impressive accuracy. In conclusion, the developed forecasting tool not only offers promise in its immediate application but also sets a robust precedent for the adoption and adaptation of similar predictive systems in various analogous use cases in the marine environment and beyond.
文摘In a prior practice and policy article published in Healthcare Science,we introduced the deployed application of an artificial intelligence(AI)model to predict longer‐term inpatient readmissions to guide community care interventions for patients with complex conditions in the context of Singapore's Hospital to Home(H2H)program that has been operating since 2017.In this follow on practice and policy article,we further elaborate on Singapore's H2H program and care model,and its supporting AI model for multiple readmission prediction,in the following ways:(1)by providing updates on the AI and supporting information systems,(2)by reporting on customer engagement and related service delivery outcomes including staff‐related time savings and patient benefits in terms of bed days saved,(3)by sharing lessons learned with respect to(i)analytics challenges encountered due to the high degree of heterogeneity and resulting variability of the data set associated with the population of program participants,(ii)balancing competing needs for simpler and stable predictive models versus continuing to further enhance models and add yet more predictive variables,and(iii)the complications of continuing to make model changes when the AI part of the system is highly interlinked with supporting clinical information systems,(4)by highlighting how this H2H effort supported broader Covid‐19 response efforts across Singapore's public healthcare system,and finally(5)by commenting on how the experiences and related capabilities acquired from running this H2H program and related community care model and supporting AI prediction model are expected to contribute to the next wave of Singapore's public healthcare efforts from 2023 onwards.For the convenience of the reader,some content that introduces the H2H program and the multiple readmissions AI prediction model that previously appeared in the prior Healthcare Science publication is repeated at the beginning of this article.
基金supported by the National Key R&D Program of China(No.2023YFE0202400)the Fujian Province Highway Open Course Subject Funding Project(No.MGSKFKT202203)+1 种基金the Fundamental Research Funds for the Central UniversitiesTongji University Innovative Research Team Grant for Humanities and Social Sciences.
文摘The advent of the artificial intelligence(AI)age offers substantial potentials for predicting regional gross domestic product(GDP)growth and transportation dynamics.This article presents an in-depth overview of the AI and empirical modeling techniques used in this area,emphasizing the significant possibilities that AI presents and discussing potential obstacles.The use of AI is essential in managing complicated data,allowing for effective analysis of detailed regional economic trends.This capacity will be essential for making economic policies and plans that respond to each region’s specific needs and capabilities.This paper first explores the relationship and impact of different modes of transportation and regional economic growth.Subsequently,various empirical models and methodological frameworks,including the factors employed for studied economic analysis were comprehensively discussed and summarized.In the last part,the discussion focuses on the potential role of AI to revolutionize regional economic research using different AI approaches.This includes its capacity to handle vast and intricate databases,its ability to forecast future patterns using historical and current data,and its assistance in advanced decision making.The present study enhances our awareness of how AI is revolutionizing the field of regional economic growth study,shedding light on both its current application and future possibilities.This study contributes to the advancement of AI predictive models in decision making for predicting regional economic growth across the globe.
文摘Predicting the material stability is essential for accelerating the discovery of advanced materials in renewable energy, aerospace, and catalysis. Traditional approaches, such as Density Functional Theory (DFT), are accurate but computationally expensive and unsuitable for high-throughput screening. This study introduces a machine learning (ML) framework trained on high-dimensional data from the Open Quantum Materials Database (OQMD) to predict formation energy, a key stability metric. Among the evaluated models, deep learning outperformed Gradient Boosting Machines and Random Forest, achieving up to 0.88 R2 prediction accuracy. Feature importance analysis identified thermodynamic, electronic, and structural properties as the primary drivers of stability, offering interpretable insights into material behavior. Compared to DFT, the proposed ML framework significantly reduces computational costs, enabling the rapid screening of thousands of compounds. These results highlight ML’s transformative potential in materials discovery, with direct applications in energy storage, semiconductors, and catalysis.
基金The work described in this paper is enabled by using the equipment funded via the EU European Regional Development Fund project entitled“Research Infrastructure for Campus-based Laboratories at the University of Rijeka–RISK”(Project RC.2.2.06-0001)the support of the University of Rijeka,Croatia,grant entitled“Advanced mechatronics devices for smart technological solutions”(Grant uniri-tehnic-18-32).
文摘A recent systematic experimental characterisation of technological thin films,based on elaborated design of experiments as well as probe calibration and correction procedures,allowed for the first time the determination of nanoscale friction under the concurrent influence of several process parameters,comprising normal forces,sliding velocities,and temperature,thus providing an indication of the intricate correlations induced by their interactions and mutual effects.This created the preconditions to undertake in this work an effort to model friction in the nanometric domain with the goal of overcoming the limitations of currently available models in ascertaining the effects of the physicochemical processes and phenomena involved in nanoscale contacts.Due to the stochastic nature of nanoscale friction and the relatively sparse available experimental data,meta-modelling tools fail,however,at predicting the factual behaviour.Based on the acquired experimental data,data mining,incorporating various state-of-the-art machine learning(ML)numerical regression algorithms,is therefore used.The results of the numerical analyses are assessed on an unseen test dataset via a comparative statistical validation.It is therefore shown that the black box ML methods provide effective predictions of the studied correlations with rather good accuracy levels,but the intrinsic nature of such algorithms prevents their usage in most practical applications.Genetic programming-based artificial intelligence(AI)methods are consequently finally used.Despite the marked complexity of the analysed phenomena and the inherent dispersion of the measurements,the developed AI-based symbolic regression models allow attaining an excellent predictive performance with the respective prediction accuracy,depending on the sample type,between 72%and 91%,allowing also to attain an extremely simple functional description of the multidimensional dependence of nanoscale friction on the studied variable process parameters.An effective tool for nanoscale friction prediction,adaptive control purposes,and further scientific and technological nanotribological analyses is thus obtained.
基金supported by grants from Guangdong Postdoctoral Research Foundation(CN)(O0390302 to SCY)National Natural Science Foundation of China(31988101 and 31730056 to YGC).
文摘Inflammatory bowel disease(IBD)is a chronic inflammatory condition caused by multiple genetic and environmental factors.Numerous genes are implicated in the etiology of IBD,but the diagnosis of IBD is challenging.Here,XGBoost,a machine learning prediction model,has been used to distinguish IBD from healthy cases following elaborative feature selection.Using combined unsupervised clustering analysis and the XGBoost feature selection method,we successfully identified a 32-gene signature that can predict IBD occurrence in new cohorts with 0.8651 accuracy.The signature shows enrichment in neutrophil extracellular trap formation and cytokine signaling in the immune system.The probability threshold of the XGBoost-based classification model can be adjusted to fit personalized lifestyle and health status.Therefore,this study reveals potential IBD-related biomarkers that facilitate an effective personalized diagnosis of IBD.
