BACKGROUND Intensive care unit-acquired weakness(ICU-AW)is a common complication that significantly impacts the patient's recovery process,even leading to adverse outcomes.Currently,there is a lack of effective pr...BACKGROUND Intensive care unit-acquired weakness(ICU-AW)is a common complication that significantly impacts the patient's recovery process,even leading to adverse outcomes.Currently,there is a lack of effective preventive measures.AIM To identify significant risk factors for ICU-AW through iterative machine learning techniques and offer recommendations for its prevention and treatment.METHODS Patients were categorized into ICU-AW and non-ICU-AW groups on the 14th day post-ICU admission.Relevant data from the initial 14 d of ICU stay,such as age,comorbidities,sedative dosage,vasopressor dosage,duration of mechanical ventilation,length of ICU stay,and rehabilitation therapy,were gathered.The relationships between these variables and ICU-AW were examined.Utilizing iterative machine learning techniques,a multilayer perceptron neural network model was developed,and its predictive performance for ICU-AW was assessed using the receiver operating characteristic curve.RESULTS Within the ICU-AW group,age,duration of mechanical ventilation,lorazepam dosage,adrenaline dosage,and length of ICU stay were significantly higher than in the non-ICU-AW group.Additionally,sepsis,multiple organ dysfunction syndrome,hypoalbuminemia,acute heart failure,respiratory failure,acute kidney injury,anemia,stress-related gastrointestinal bleeding,shock,hypertension,coronary artery disease,malignant tumors,and rehabilitation therapy ratios were significantly higher in the ICU-AW group,demonstrating statistical significance.The most influential factors contributing to ICU-AW were identified as the length of ICU stay(100.0%)and the duration of mechanical ventilation(54.9%).The neural network model predicted ICU-AW with an area under the curve of 0.941,sensitivity of 92.2%,and specificity of 82.7%.CONCLUSION The main factors influencing ICU-AW are the length of ICU stay and the duration of mechanical ventilation.A primary preventive strategy,when feasible,involves minimizing both ICU stay and mechanical ventilation duration.展开更多
Jet grouting is one of the most popular soil improvement techniques,but its design usually involves great uncertainties that can lead to economic cost overruns in construction projects.The high dispersion in the prope...Jet grouting is one of the most popular soil improvement techniques,but its design usually involves great uncertainties that can lead to economic cost overruns in construction projects.The high dispersion in the properties of the improved material leads to designers assuming a conservative,arbitrary and unjustified strength,which is even sometimes subjected to the results of the test fields.The present paper presents an approach for prediction of the uniaxial compressive strength(UCS)of jet grouting columns based on the analysis of several machine learning algorithms on a database of 854 results mainly collected from different research papers.The selected machine learning model(extremely randomized trees)relates the soil type and various parameters of the technique to the value of the compressive strength.Despite the complex mechanism that surrounds the jet grouting process,evidenced by the high dispersion and low correlation of the variables studied,the trained model allows to optimally predict the values of compressive strength with a significant improvement with respect to the existing works.Consequently,this work proposes for the first time a reliable and easily applicable approach for estimation of the compressive strength of jet grouting columns.展开更多
Stroke is a leading cause of disability and mortality worldwide,necessitating the development of advanced technologies to improve its diagnosis,treatment,and patient outcomes.In recent years,machine learning technique...Stroke is a leading cause of disability and mortality worldwide,necessitating the development of advanced technologies to improve its diagnosis,treatment,and patient outcomes.In recent years,machine learning techniques have emerged as promising tools in stroke medicine,enabling efficient analysis of large-scale datasets and facilitating personalized and precision medicine approaches.This abstract provides a comprehensive overview of machine learning’s applications,challenges,and future directions in stroke medicine.Recently introduced machine learning algorithms have been extensively employed in all the fields of stroke medicine.Machine learning models have demonstrated remarkable accuracy in imaging analysis,diagnosing stroke subtypes,risk stratifications,guiding medical treatment,and predicting patient prognosis.Despite the tremendous potential of machine learning in stroke medicine,several challenges must be addressed.These include the need for standardized and interoperable data collection,robust model validation and generalization,and the ethical considerations surrounding privacy and bias.In addition,integrating machine learning models into clinical workflows and establishing regulatory frameworks are critical for ensuring their widespread adoption and impact in routine stroke care.Machine learning promises to revolutionize stroke medicine by enabling precise diagnosis,tailored treatment selection,and improved prognostication.Continued research and collaboration among clinicians,researchers,and technologists are essential for overcoming challenges and realizing the full potential of machine learning in stroke care,ultimately leading to enhanced patient outcomes and quality of life.This review aims to summarize all the current implications of machine learning in stroke diagnosis,treatment,and prognostic evaluation.At the same time,another purpose of this paper is to explore all the future perspectives these techniques can provide in combating this disabling disease.展开更多
BACKGROUND Liver transplantation(LT)is a life-saving intervention for patients with end-stage liver disease.However,the equitable allocation of scarce donor organs remains a formidable challenge.Prognostic tools are p...BACKGROUND Liver transplantation(LT)is a life-saving intervention for patients with end-stage liver disease.However,the equitable allocation of scarce donor organs remains a formidable challenge.Prognostic tools are pivotal in identifying the most suitable transplant candidates.Traditionally,scoring systems like the model for end-stage liver disease have been instrumental in this process.Nevertheless,the landscape of prognostication is undergoing a transformation with the integration of machine learning(ML)and artificial intelligence models.AIM To assess the utility of ML models in prognostication for LT,comparing their performance and reliability to established traditional scoring systems.METHODS Following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines,we conducted a thorough and standardized literature search using the PubMed/MEDLINE database.Our search imposed no restrictions on publication year,age,or gender.Exclusion criteria encompassed non-English studies,review articles,case reports,conference papers,studies with missing data,or those exhibiting evident methodological flaws.RESULTS Our search yielded a total of 64 articles,with 23 meeting the inclusion criteria.Among the selected studies,60.8%originated from the United States and China combined.Only one pediatric study met the criteria.Notably,91%of the studies were published within the past five years.ML models consistently demonstrated satisfactory to excellent area under the receiver operating characteristic curve values(ranging from 0.6 to 1)across all studies,surpassing the performance of traditional scoring systems.Random forest exhibited superior predictive capabilities for 90-d mortality following LT,sepsis,and acute kidney injury(AKI).In contrast,gradient boosting excelled in predicting the risk of graft-versus-host disease,pneumonia,and AKI.CONCLUSION This study underscores the potential of ML models in guiding decisions related to allograft allocation and LT,marking a significant evolution in the field of prognostication.展开更多
Journal of Future Foods(ISSN 2772-5669.Owner:Beijing Academy of Food Sciences.Production and Hosting:Elsevier B.V.on behalf of KeAi Communications Co.,Ltd.)is an international,peer-reviewed open access journal belongi...Journal of Future Foods(ISSN 2772-5669.Owner:Beijing Academy of Food Sciences.Production and Hosting:Elsevier B.V.on behalf of KeAi Communications Co.,Ltd.)is an international,peer-reviewed open access journal belonging to the discipline of food science and technology.The aim of the journal is to report latest research results of high-tech in food science.We welcome submissions that drive the field of food science towards whole food nutrition,intelligence and high technology.展开更多
Geodesy and Geodynamics mainly publishes the newest research in the Geodesy and Geodynamics.The journal has been indexed by Ei Compendex,Scopus,ESCI,CSCD,NASA ADS,Geobase,GeoRef Preview database,Ulrich's Periodica...Geodesy and Geodynamics mainly publishes the newest research in the Geodesy and Geodynamics.The journal has been indexed by Ei Compendex,Scopus,ESCI,CSCD,NASA ADS,Geobase,GeoRef Preview database,Ulrich's Periodical Directory,VINITI databse.All the papers accepted by the journal are open access on the ScienceDirect with free charge(ttp://ww.sciencedirect.com/science/journa/6749847.)展开更多
Geodesy and Geodynamics mainly publishes the newest research in the Geodesy and Geodynamics.The journal has been indexed by Ei Compendex,Scopus,ESCl,CSCD,NASA ADS,Geobase,GeoRef Preview database,Ulrich's Periodica...Geodesy and Geodynamics mainly publishes the newest research in the Geodesy and Geodynamics.The journal has been indexed by Ei Compendex,Scopus,ESCl,CSCD,NASA ADS,Geobase,GeoRef Preview database,Ulrich's Periodical Directory,VINITI databse.All the papers accepted by the journal are open access on the ScienceDirect with free charge(http://www.sciencedirect.com/science/journal/16749847).展开更多
Geodesy and Geodynamics mainly publishes the newest research in the Geodesy and Geodynamics.The journal has been indexed by Ei Compendex,Scopus,ESCl,CSCD,NASA ADS,Geobase,GeoRef Preview database,Ulrich's Periodica...Geodesy and Geodynamics mainly publishes the newest research in the Geodesy and Geodynamics.The journal has been indexed by Ei Compendex,Scopus,ESCl,CSCD,NASA ADS,Geobase,GeoRef Preview database,Ulrich's Periodical Directory,VINITI databse.All the papers accepted by the journal are open access on the ScienceDirect with free charge(http://www.sciencedirect.com/science/journal/16749847).展开更多
Journal of Future Foods(ISSN 2772-5669.Owner:Beijing Academy of Food Sciences.Production and Hosting:Elsevier B.V.on behalf of KeAi Communications Co.,Ltd.)is an international,peer-reviewed open access journal belongi...Journal of Future Foods(ISSN 2772-5669.Owner:Beijing Academy of Food Sciences.Production and Hosting:Elsevier B.V.on behalf of KeAi Communications Co.,Ltd.)is an international,peer-reviewed open access journal belonging to the discipline of food science and technology.The aim of the journal is to report latest research results of high-tech in food science.We welcome submissions that drive the field of food science towards whole food nutrition,intelligence and high technology.展开更多
Journal of Future Foods(ISSN 2772-5669.Owner:Beijing Academy of Food Sciences.Production and Hosting:Elsevier B.V.on behalf of KeAi Communications Co.,Ltd.)is an international,peer-reviewed open access journal belongi...Journal of Future Foods(ISSN 2772-5669.Owner:Beijing Academy of Food Sciences.Production and Hosting:Elsevier B.V.on behalf of KeAi Communications Co.,Ltd.)is an international,peer-reviewed open access journal belonging to the discipline of food science and technology.The aim of the journal is to report latest research results of high-tech in food science.We welcome submissions that drive the field of food science towards whole food nutrition,intelligence and high technology.展开更多
FoodsJournal of Future Foods(ISSN 2772-5669.Owner:Beijing Academy of Food Sciences.Production and Hosting:Elsevier B.V.on behalf of KeAi Communications Co.,Ltd.)is an international,peer-reviewed open access journal be...FoodsJournal of Future Foods(ISSN 2772-5669.Owner:Beijing Academy of Food Sciences.Production and Hosting:Elsevier B.V.on behalf of KeAi Communications Co.,Ltd.)is an international,peer-reviewed open access journal belonging to the discipline of food science and technology.The aim of the journal is to report latest research results of high-tech in food science.We welcome submissions that drive the field of food science towards whole food nutrition,intelligence and high technology.展开更多
Journal of Future Foods(ISSN 2772-5669.Owner:Beijing Academy of Food Sciences.Production and Hosting:Elsevier B.V.on behalf of KeAi Communications Co.,Ltd.)is an international,peer-reviewed open access journal belongi...Journal of Future Foods(ISSN 2772-5669.Owner:Beijing Academy of Food Sciences.Production and Hosting:Elsevier B.V.on behalf of KeAi Communications Co.,Ltd.)is an international,peer-reviewed open access journal belonging to the discipline of food science and technology.The aim of the journal is to report latest research results of high-tech in food science.We welcome submissions that drive the field of food science towards whole food nutrition,intelligence and high technology.展开更多
Journal of Future Foods(ISSN 2772-5669.Owner:Beijing Academy of Food Sciences.Production and Hosting:Elsevier B.V.on behalf of KeAi Communications Co.,Ltd.)is an international,peer-reviewed open access journal belongi...Journal of Future Foods(ISSN 2772-5669.Owner:Beijing Academy of Food Sciences.Production and Hosting:Elsevier B.V.on behalf of KeAi Communications Co.,Ltd.)is an international,peer-reviewed open access journal belonging to the discipline of food science and technology.The aim of the journal is to report latest research results of high-tech in food science.We welcome submissions that drive the field of food science towards whole food nutrition,intelligence and high technology.展开更多
Journal of Future Foods(ISSN 2772-5669.Owner:Beijing Academy of Food Sciences.Production and Hosting:Elsevier B.V.on behalf of KeAi Communications Co.,Ltd.)is an international,peer-reviewed open access journal belongi...Journal of Future Foods(ISSN 2772-5669.Owner:Beijing Academy of Food Sciences.Production and Hosting:Elsevier B.V.on behalf of KeAi Communications Co.,Ltd.)is an international,peer-reviewed open access journal belonging to the discipline of food science and technology.The aim of the journal is to report latest research results of high-tech in food science.We welcome submissions that drive the field of food science towards whole food nutrition,intelligence and high technology.展开更多
Machine learning(ML) is well suited for the prediction of high-complexity,high-dimensional problems such as those encountered in terminal ballistics.We evaluate the performance of four popular ML-based regression mode...Machine learning(ML) is well suited for the prediction of high-complexity,high-dimensional problems such as those encountered in terminal ballistics.We evaluate the performance of four popular ML-based regression models,extreme gradient boosting(XGBoost),artificial neural network(ANN),support vector regression(SVR),and Gaussian process regression(GP),on two common terminal ballistics’ problems:(a)predicting the V50ballistic limit of monolithic metallic armour impacted by small and medium calibre projectiles and fragments,and(b) predicting the depth to which a projectile will penetrate a target of semi-infinite thickness.To achieve this we utilise two datasets,each consisting of approximately 1000samples,collated from public release sources.We demonstrate that all four model types provide similarly excellent agreement when interpolating within the training data and diverge when extrapolating outside this range.Although extrapolation is not advisable for ML-based regression models,for applications such as lethality/survivability analysis,such capability is required.To circumvent this,we implement expert knowledge and physics-based models via enforced monotonicity,as a Gaussian prior mean,and through a modified loss function.The physics-informed models demonstrate improved performance over both classical physics-based models and the basic ML regression models,providing an ability to accurately fit experimental data when it is available and then revert to the physics-based model when not.The resulting models demonstrate high levels of predictive accuracy over a very wide range of projectile types,target materials and thicknesses,and impact conditions significantly more diverse than that achievable from any existing analytical approach.Compared with numerical analysis tools such as finite element solvers the ML models run orders of magnitude faster.We provide some general guidelines throughout for the development,application,and reporting of ML models in terminal ballistics problems.展开更多
Journal of Future Foods(ISSN 2772-5669.Owner:Beijing Academy of Food Sciences.Production and Hosting:Elsevier B.V.on behalf of KeAi Communications Co.,Ltd.)is an international,peer-reviewed open access journal belongi...Journal of Future Foods(ISSN 2772-5669.Owner:Beijing Academy of Food Sciences.Production and Hosting:Elsevier B.V.on behalf of KeAi Communications Co.,Ltd.)is an international,peer-reviewed open access journal belonging to the discipline of food science and technology.The aim of the journal is to report latest research results of high-tech in food science.We welcome submissions that drive the field of food science towards whole food nutrition,intelligence and high technology.展开更多
Wi Fi and fingerprinting localization method have been a hot topic in indoor positioning because of their universality and location-related features.The basic assumption of fingerprinting localization is that the rece...Wi Fi and fingerprinting localization method have been a hot topic in indoor positioning because of their universality and location-related features.The basic assumption of fingerprinting localization is that the received signal strength indication(RSSI)distance is accord with the location distance.Therefore,how to efficiently match the current RSSI of the user with the RSSI in the fingerprint database is the key to achieve high-accuracy localization.In this paper,a particle swarm optimization-extreme learning machine(PSO-ELM)algorithm is proposed on the basis of the original fingerprinting localization.Firstly,we collect the RSSI of the experimental area to construct the fingerprint database,and the ELM algorithm is applied to the online stages to determine the corresponding relation between the location of the terminal and the RSSI it receives.Secondly,PSO algorithm is used to improve the bias and weight of ELM neural network,and the global optimal results are obtained.Finally,extensive simulation results are presented.It is shown that the proposed algorithm can effectively reduce mean error of localization and improve positioning accuracy when compared with K-Nearest Neighbor(KNN),Kmeans and Back-propagation(BP)algorithms.展开更多
The support vector machine(SVM)is a classical machine learning method.Both the hinge loss and least absolute shrinkage and selection operator(LASSO)penalty are usually used in traditional SVMs.However,the hinge loss i...The support vector machine(SVM)is a classical machine learning method.Both the hinge loss and least absolute shrinkage and selection operator(LASSO)penalty are usually used in traditional SVMs.However,the hinge loss is not differentiable,and the LASSO penalty does not have the Oracle property.In this paper,the huberized loss is combined with non-convex penalties to obtain a model that has the advantages of both the computational simplicity and the Oracle property,contributing to higher accuracy than traditional SVMs.It is experimentally demonstrated that the two non-convex huberized-SVM methods,smoothly clipped absolute deviation huberized-SVM(SCAD-HSVM)and minimax concave penalty huberized-SVM(MCP-HSVM),outperform the traditional SVM method in terms of the prediction accuracy and classifier performance.They are also superior in terms of variable selection,especially when there is a high linear correlation between the variables.When they are applied to the prediction of listed companies,the variables that can affect and predict financial distress are accurately filtered out.Among all the indicators,the indicators per share have the greatest influence while those of solvency have the weakest influence.Listed companies can assess the financial situation with the indicators screened by our algorithm and make an early warning of their possible financial distress in advance with higher precision.展开更多
Additive manufacturing technology is highly regarded due to its advantages,such as high precision and the ability to address complex geometric challenges.However,the development of additive manufacturing process is co...Additive manufacturing technology is highly regarded due to its advantages,such as high precision and the ability to address complex geometric challenges.However,the development of additive manufacturing process is constrained by issues like unclear fundamental principles,complex experimental cycles,and high costs.Machine learning,as a novel artificial intelligence technology,has the potential to deeply engage in the development of additive manufacturing process,assisting engineers in learning and developing new techniques.This paper provides a comprehensive overview of the research and applications of machine learning in the field of additive manufacturing,particularly in model design and process development.Firstly,it introduces the background and significance of machine learning-assisted design in additive manufacturing process.It then further delves into the application of machine learning in additive manufacturing,focusing on model design and process guidance.Finally,it concludes by summarizing and forecasting the development trends of machine learning technology in the field of additive manufacturing.展开更多
基金Supported by Science and Technology Support Program of Qiandongnan Prefecture,No.Qiandongnan Sci-Tech Support[2021]12Guizhou Province High-Level Innovative Talent Training Program,No.Qiannan Thousand Talents[2022]201701.
文摘BACKGROUND Intensive care unit-acquired weakness(ICU-AW)is a common complication that significantly impacts the patient's recovery process,even leading to adverse outcomes.Currently,there is a lack of effective preventive measures.AIM To identify significant risk factors for ICU-AW through iterative machine learning techniques and offer recommendations for its prevention and treatment.METHODS Patients were categorized into ICU-AW and non-ICU-AW groups on the 14th day post-ICU admission.Relevant data from the initial 14 d of ICU stay,such as age,comorbidities,sedative dosage,vasopressor dosage,duration of mechanical ventilation,length of ICU stay,and rehabilitation therapy,were gathered.The relationships between these variables and ICU-AW were examined.Utilizing iterative machine learning techniques,a multilayer perceptron neural network model was developed,and its predictive performance for ICU-AW was assessed using the receiver operating characteristic curve.RESULTS Within the ICU-AW group,age,duration of mechanical ventilation,lorazepam dosage,adrenaline dosage,and length of ICU stay were significantly higher than in the non-ICU-AW group.Additionally,sepsis,multiple organ dysfunction syndrome,hypoalbuminemia,acute heart failure,respiratory failure,acute kidney injury,anemia,stress-related gastrointestinal bleeding,shock,hypertension,coronary artery disease,malignant tumors,and rehabilitation therapy ratios were significantly higher in the ICU-AW group,demonstrating statistical significance.The most influential factors contributing to ICU-AW were identified as the length of ICU stay(100.0%)and the duration of mechanical ventilation(54.9%).The neural network model predicted ICU-AW with an area under the curve of 0.941,sensitivity of 92.2%,and specificity of 82.7%.CONCLUSION The main factors influencing ICU-AW are the length of ICU stay and the duration of mechanical ventilation.A primary preventive strategy,when feasible,involves minimizing both ICU stay and mechanical ventilation duration.
基金This work has been supported by the Conselleria de Inno-vación,Universidades,Ciencia y Sociedad Digital de la Generalitat Valenciana(CIAICO/2021/335).
文摘Jet grouting is one of the most popular soil improvement techniques,but its design usually involves great uncertainties that can lead to economic cost overruns in construction projects.The high dispersion in the properties of the improved material leads to designers assuming a conservative,arbitrary and unjustified strength,which is even sometimes subjected to the results of the test fields.The present paper presents an approach for prediction of the uniaxial compressive strength(UCS)of jet grouting columns based on the analysis of several machine learning algorithms on a database of 854 results mainly collected from different research papers.The selected machine learning model(extremely randomized trees)relates the soil type and various parameters of the technique to the value of the compressive strength.Despite the complex mechanism that surrounds the jet grouting process,evidenced by the high dispersion and low correlation of the variables studied,the trained model allows to optimally predict the values of compressive strength with a significant improvement with respect to the existing works.Consequently,this work proposes for the first time a reliable and easily applicable approach for estimation of the compressive strength of jet grouting columns.
文摘Stroke is a leading cause of disability and mortality worldwide,necessitating the development of advanced technologies to improve its diagnosis,treatment,and patient outcomes.In recent years,machine learning techniques have emerged as promising tools in stroke medicine,enabling efficient analysis of large-scale datasets and facilitating personalized and precision medicine approaches.This abstract provides a comprehensive overview of machine learning’s applications,challenges,and future directions in stroke medicine.Recently introduced machine learning algorithms have been extensively employed in all the fields of stroke medicine.Machine learning models have demonstrated remarkable accuracy in imaging analysis,diagnosing stroke subtypes,risk stratifications,guiding medical treatment,and predicting patient prognosis.Despite the tremendous potential of machine learning in stroke medicine,several challenges must be addressed.These include the need for standardized and interoperable data collection,robust model validation and generalization,and the ethical considerations surrounding privacy and bias.In addition,integrating machine learning models into clinical workflows and establishing regulatory frameworks are critical for ensuring their widespread adoption and impact in routine stroke care.Machine learning promises to revolutionize stroke medicine by enabling precise diagnosis,tailored treatment selection,and improved prognostication.Continued research and collaboration among clinicians,researchers,and technologists are essential for overcoming challenges and realizing the full potential of machine learning in stroke care,ultimately leading to enhanced patient outcomes and quality of life.This review aims to summarize all the current implications of machine learning in stroke diagnosis,treatment,and prognostic evaluation.At the same time,another purpose of this paper is to explore all the future perspectives these techniques can provide in combating this disabling disease.
文摘BACKGROUND Liver transplantation(LT)is a life-saving intervention for patients with end-stage liver disease.However,the equitable allocation of scarce donor organs remains a formidable challenge.Prognostic tools are pivotal in identifying the most suitable transplant candidates.Traditionally,scoring systems like the model for end-stage liver disease have been instrumental in this process.Nevertheless,the landscape of prognostication is undergoing a transformation with the integration of machine learning(ML)and artificial intelligence models.AIM To assess the utility of ML models in prognostication for LT,comparing their performance and reliability to established traditional scoring systems.METHODS Following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines,we conducted a thorough and standardized literature search using the PubMed/MEDLINE database.Our search imposed no restrictions on publication year,age,or gender.Exclusion criteria encompassed non-English studies,review articles,case reports,conference papers,studies with missing data,or those exhibiting evident methodological flaws.RESULTS Our search yielded a total of 64 articles,with 23 meeting the inclusion criteria.Among the selected studies,60.8%originated from the United States and China combined.Only one pediatric study met the criteria.Notably,91%of the studies were published within the past five years.ML models consistently demonstrated satisfactory to excellent area under the receiver operating characteristic curve values(ranging from 0.6 to 1)across all studies,surpassing the performance of traditional scoring systems.Random forest exhibited superior predictive capabilities for 90-d mortality following LT,sepsis,and acute kidney injury(AKI).In contrast,gradient boosting excelled in predicting the risk of graft-versus-host disease,pneumonia,and AKI.CONCLUSION This study underscores the potential of ML models in guiding decisions related to allograft allocation and LT,marking a significant evolution in the field of prognostication.
文摘Journal of Future Foods(ISSN 2772-5669.Owner:Beijing Academy of Food Sciences.Production and Hosting:Elsevier B.V.on behalf of KeAi Communications Co.,Ltd.)is an international,peer-reviewed open access journal belonging to the discipline of food science and technology.The aim of the journal is to report latest research results of high-tech in food science.We welcome submissions that drive the field of food science towards whole food nutrition,intelligence and high technology.
文摘Geodesy and Geodynamics mainly publishes the newest research in the Geodesy and Geodynamics.The journal has been indexed by Ei Compendex,Scopus,ESCI,CSCD,NASA ADS,Geobase,GeoRef Preview database,Ulrich's Periodical Directory,VINITI databse.All the papers accepted by the journal are open access on the ScienceDirect with free charge(ttp://ww.sciencedirect.com/science/journa/6749847.)
文摘Geodesy and Geodynamics mainly publishes the newest research in the Geodesy and Geodynamics.The journal has been indexed by Ei Compendex,Scopus,ESCl,CSCD,NASA ADS,Geobase,GeoRef Preview database,Ulrich's Periodical Directory,VINITI databse.All the papers accepted by the journal are open access on the ScienceDirect with free charge(http://www.sciencedirect.com/science/journal/16749847).
文摘Geodesy and Geodynamics mainly publishes the newest research in the Geodesy and Geodynamics.The journal has been indexed by Ei Compendex,Scopus,ESCl,CSCD,NASA ADS,Geobase,GeoRef Preview database,Ulrich's Periodical Directory,VINITI databse.All the papers accepted by the journal are open access on the ScienceDirect with free charge(http://www.sciencedirect.com/science/journal/16749847).
文摘Journal of Future Foods(ISSN 2772-5669.Owner:Beijing Academy of Food Sciences.Production and Hosting:Elsevier B.V.on behalf of KeAi Communications Co.,Ltd.)is an international,peer-reviewed open access journal belonging to the discipline of food science and technology.The aim of the journal is to report latest research results of high-tech in food science.We welcome submissions that drive the field of food science towards whole food nutrition,intelligence and high technology.
文摘Journal of Future Foods(ISSN 2772-5669.Owner:Beijing Academy of Food Sciences.Production and Hosting:Elsevier B.V.on behalf of KeAi Communications Co.,Ltd.)is an international,peer-reviewed open access journal belonging to the discipline of food science and technology.The aim of the journal is to report latest research results of high-tech in food science.We welcome submissions that drive the field of food science towards whole food nutrition,intelligence and high technology.
文摘FoodsJournal of Future Foods(ISSN 2772-5669.Owner:Beijing Academy of Food Sciences.Production and Hosting:Elsevier B.V.on behalf of KeAi Communications Co.,Ltd.)is an international,peer-reviewed open access journal belonging to the discipline of food science and technology.The aim of the journal is to report latest research results of high-tech in food science.We welcome submissions that drive the field of food science towards whole food nutrition,intelligence and high technology.
文摘Journal of Future Foods(ISSN 2772-5669.Owner:Beijing Academy of Food Sciences.Production and Hosting:Elsevier B.V.on behalf of KeAi Communications Co.,Ltd.)is an international,peer-reviewed open access journal belonging to the discipline of food science and technology.The aim of the journal is to report latest research results of high-tech in food science.We welcome submissions that drive the field of food science towards whole food nutrition,intelligence and high technology.
文摘Journal of Future Foods(ISSN 2772-5669.Owner:Beijing Academy of Food Sciences.Production and Hosting:Elsevier B.V.on behalf of KeAi Communications Co.,Ltd.)is an international,peer-reviewed open access journal belonging to the discipline of food science and technology.The aim of the journal is to report latest research results of high-tech in food science.We welcome submissions that drive the field of food science towards whole food nutrition,intelligence and high technology.
文摘Journal of Future Foods(ISSN 2772-5669.Owner:Beijing Academy of Food Sciences.Production and Hosting:Elsevier B.V.on behalf of KeAi Communications Co.,Ltd.)is an international,peer-reviewed open access journal belonging to the discipline of food science and technology.The aim of the journal is to report latest research results of high-tech in food science.We welcome submissions that drive the field of food science towards whole food nutrition,intelligence and high technology.
文摘Machine learning(ML) is well suited for the prediction of high-complexity,high-dimensional problems such as those encountered in terminal ballistics.We evaluate the performance of four popular ML-based regression models,extreme gradient boosting(XGBoost),artificial neural network(ANN),support vector regression(SVR),and Gaussian process regression(GP),on two common terminal ballistics’ problems:(a)predicting the V50ballistic limit of monolithic metallic armour impacted by small and medium calibre projectiles and fragments,and(b) predicting the depth to which a projectile will penetrate a target of semi-infinite thickness.To achieve this we utilise two datasets,each consisting of approximately 1000samples,collated from public release sources.We demonstrate that all four model types provide similarly excellent agreement when interpolating within the training data and diverge when extrapolating outside this range.Although extrapolation is not advisable for ML-based regression models,for applications such as lethality/survivability analysis,such capability is required.To circumvent this,we implement expert knowledge and physics-based models via enforced monotonicity,as a Gaussian prior mean,and through a modified loss function.The physics-informed models demonstrate improved performance over both classical physics-based models and the basic ML regression models,providing an ability to accurately fit experimental data when it is available and then revert to the physics-based model when not.The resulting models demonstrate high levels of predictive accuracy over a very wide range of projectile types,target materials and thicknesses,and impact conditions significantly more diverse than that achievable from any existing analytical approach.Compared with numerical analysis tools such as finite element solvers the ML models run orders of magnitude faster.We provide some general guidelines throughout for the development,application,and reporting of ML models in terminal ballistics problems.
文摘Journal of Future Foods(ISSN 2772-5669.Owner:Beijing Academy of Food Sciences.Production and Hosting:Elsevier B.V.on behalf of KeAi Communications Co.,Ltd.)is an international,peer-reviewed open access journal belonging to the discipline of food science and technology.The aim of the journal is to report latest research results of high-tech in food science.We welcome submissions that drive the field of food science towards whole food nutrition,intelligence and high technology.
基金supported in part by the National Natural Science Foundation of China(U2001213 and 61971191)in part by the Beijing Natural Science Foundation under Grant L182018 and L201011+2 种基金in part by National Key Research and Development Project(2020YFB1807204)in part by the Key project of Natural Science Foundation of Jiangxi Province(20202ACBL202006)in part by the Innovation Fund Designated for Graduate Students of Jiangxi Province(YC2020-S321)。
文摘Wi Fi and fingerprinting localization method have been a hot topic in indoor positioning because of their universality and location-related features.The basic assumption of fingerprinting localization is that the received signal strength indication(RSSI)distance is accord with the location distance.Therefore,how to efficiently match the current RSSI of the user with the RSSI in the fingerprint database is the key to achieve high-accuracy localization.In this paper,a particle swarm optimization-extreme learning machine(PSO-ELM)algorithm is proposed on the basis of the original fingerprinting localization.Firstly,we collect the RSSI of the experimental area to construct the fingerprint database,and the ELM algorithm is applied to the online stages to determine the corresponding relation between the location of the terminal and the RSSI it receives.Secondly,PSO algorithm is used to improve the bias and weight of ELM neural network,and the global optimal results are obtained.Finally,extensive simulation results are presented.It is shown that the proposed algorithm can effectively reduce mean error of localization and improve positioning accuracy when compared with K-Nearest Neighbor(KNN),Kmeans and Back-propagation(BP)algorithms.
文摘The support vector machine(SVM)is a classical machine learning method.Both the hinge loss and least absolute shrinkage and selection operator(LASSO)penalty are usually used in traditional SVMs.However,the hinge loss is not differentiable,and the LASSO penalty does not have the Oracle property.In this paper,the huberized loss is combined with non-convex penalties to obtain a model that has the advantages of both the computational simplicity and the Oracle property,contributing to higher accuracy than traditional SVMs.It is experimentally demonstrated that the two non-convex huberized-SVM methods,smoothly clipped absolute deviation huberized-SVM(SCAD-HSVM)and minimax concave penalty huberized-SVM(MCP-HSVM),outperform the traditional SVM method in terms of the prediction accuracy and classifier performance.They are also superior in terms of variable selection,especially when there is a high linear correlation between the variables.When they are applied to the prediction of listed companies,the variables that can affect and predict financial distress are accurately filtered out.Among all the indicators,the indicators per share have the greatest influence while those of solvency have the weakest influence.Listed companies can assess the financial situation with the indicators screened by our algorithm and make an early warning of their possible financial distress in advance with higher precision.
基金financially supported by the Technology Development Fund of China Academy of Machinery Science and Technology(No.170221ZY01)。
文摘Additive manufacturing technology is highly regarded due to its advantages,such as high precision and the ability to address complex geometric challenges.However,the development of additive manufacturing process is constrained by issues like unclear fundamental principles,complex experimental cycles,and high costs.Machine learning,as a novel artificial intelligence technology,has the potential to deeply engage in the development of additive manufacturing process,assisting engineers in learning and developing new techniques.This paper provides a comprehensive overview of the research and applications of machine learning in the field of additive manufacturing,particularly in model design and process development.Firstly,it introduces the background and significance of machine learning-assisted design in additive manufacturing process.It then further delves into the application of machine learning in additive manufacturing,focusing on model design and process guidance.Finally,it concludes by summarizing and forecasting the development trends of machine learning technology in the field of additive manufacturing.