Industrial intelligence is a core technology in the upgrading of the production processes and management modes of traditional industries.Motivated by the major development strategies and needs of industrial intellectu...Industrial intelligence is a core technology in the upgrading of the production processes and management modes of traditional industries.Motivated by the major development strategies and needs of industrial intellectualization in China,this study presents an innovative fusion structure that encompasses the theoretical foundation and technological innovation of data analytics and optimization,as well as their application to smart industrial engineering.First,this study describes a general methodology for the fusion of data analytics and optimization.Then,it identifies some data analytics and system optimization technologies to handle key issues in smart manufacturing.Finally,it provides a four-level framework for smart industry based on the theoretical and technological research on the fusion of data analytics and optimization.The framework uses data analytics to perceive and analyze industrial production and logistics processes.It also demonstrates the intelligent capability of planning,scheduling,operation optimization,and optimal control.Data analytics and system optimization technologies are employed in the four-level framework to overcome some critical issues commonly faced by manufacturing,resources and materials,energy,and logistics systems,such as high energy consumption,high costs,low energy efficiency,low resource utilization,and serious environmental pollution.The fusion of data analytics and optimization allows enterprises to enhance the prediction and control of unknown areas and discover hidden knowledge to improve decision-making efficiency。Therefore,industrial intelligence has great importance in China’s industrial upgrading and transformation into a true industrial power.展开更多
Sterile neutrinos can influence the evolution of the Universe,and thus cosmological observations can be used to detect them.Future gravitational-wave(GW)observations can precisely measure absolute cosmological distanc...Sterile neutrinos can influence the evolution of the Universe,and thus cosmological observations can be used to detect them.Future gravitational-wave(GW)observations can precisely measure absolute cosmological distances,helping to break parameter degeneracies generated by traditional cosmological observations.This advancement can lead to much tighter constraints on sterile neutrino parameters.This work provides a preliminary forecast for detecting sterile neutrinos using third-generation GW detectors in combination with future shortγ-ray burst observations from a THESEUS-like telescope,an approach not previously explored in the literature.Both massless and massive sterile neutrinos are considered within theΛCDM cosmology.We find that using GW data can greatly enhance the detection capability for massless sterile neutrinos,reaching 3σlevel.For massive sterile neutrinos,GW data can also greatly assist in improving the parameter constraints,but it seems that effective detection is still not feasible.展开更多
In this paper,we explore the ever-changing field ofDigital Twins(DT)in the Industrial Internet of Things(IIoT)context,emphasizing their critical role in advancing Industry 4.0 toward the frontiers of Industry 5.0.The ...In this paper,we explore the ever-changing field ofDigital Twins(DT)in the Industrial Internet of Things(IIoT)context,emphasizing their critical role in advancing Industry 4.0 toward the frontiers of Industry 5.0.The article explores the applications of DT in several industrial sectors and their smooth integration into the IIoT,focusing on the fundamentals of digital twins and emphasizing the importance of virtual-real integration.It discusses the emergence of DT,contextualizing its evolution within the framework of IIoT.The study categorizes the different types of DT,including prototypes and instances,and provides an in-depth analysis of the enabling technologies such as IoT,Artificial Intelligence(AI),Extended Reality(XR),cloud computing,and the Application Programming Interface(API).The paper demonstrates theDT advantages through the practical integration of real-world case studies,which highlights the technology’s exceptional capacity to improve traceability and fault detection within the context of the IIoT.This paper offers a focused,application-driven perspective on DTs in IIoT,specifically highlighting their role in key production phases such as designing,intelligent manufacturing,maintenance,resource management,automation,security,and safety.By emphasizing their potential to support human-centric,sustainable advancements in Industry 5.0,this study distinguishes itself from existing literature.It provides valuable insights that connect theoretical advancements with practical implementation,making it a crucial resource for researchers,practitioners,and industry professionals.展开更多
Objective To explore potential keywords,research clusters,collaborative pattern,and research trends in the field of medical technology management(MTM)through bibliometric analysis,providing insights for researchers,po...Objective To explore potential keywords,research clusters,collaborative pattern,and research trends in the field of medical technology management(MTM)through bibliometric analysis,providing insights for researchers,policy makers,and hospital administrators.Methods A retrieval formula was applied to the title,abstract,and keywords in the Web of Science(WoS)Core Collection,along with system-recommended terms,to identify articles on MTM.A total of 181 articles published between 1974 and 2022 were retained for quantitative analysis.The global trend of research output;total citations,average citations,and H-index;and bibliographic coupling,co-authorship,and keyword co-occurrence were analyzed using VOSviewer.Results The number of articles on MTM has been steadily increasing year by year.The focus of research has shifted from addressing basic medical needs to prioritizing emergency response and medical information security.The United States,Italy,and the United Kingdom emerged as the main contributors,with the United States leading in both volume of publications(60 articles)and academic impact(H-index=21).Authors from the United Kingdom and the United States led the way in cross-border cooperation.The top five institutions,ranked by total link strength among cross-institutional authors,were primarily located in Canada and Spain.Conclusions The field of MTM has experienced stable growth over the past three decades(1993-2022).The shift of research focus has prompted a heightened emphasis on protecting patient privacy and ensuring the security of medical data.Future research should emphasize interdisciplinary and professional collaboration,as well as international cooperation and open sharing of knowledge.展开更多
Introduction:Consumer wearables increasingly provide users with Composite Health Scores(CHS)–integrated biometric indices that claim to quantify readiness,recovery,stress,or overall well-being.Despite their growing a...Introduction:Consumer wearables increasingly provide users with Composite Health Scores(CHS)–integrated biometric indices that claim to quantify readiness,recovery,stress,or overall well-being.Despite their growing adoption,the validity,transparency,and physiological relevance of these scores remain unclear.This study systematically evaluates CHS fromleading wearablemanufacturers to assess their underlying methodologies,contributors,and scientific basis.Content:Information was synthesised from publicly available company documentation,including technical white papers,user manuals,app interfaces,and research literature where available.We identified 14 CHS across 10 major wearable manufacturers,including Fitbit(Daily Readiness),Garmin(Body Battery^(TM)and Training Readiness),Oura(Readiness and Resilience),WHOOP(Strain,Recovery,and Stress Monitor),Polar(Nightly Recharge^(TM)),Samsung(Energy Score),Suunto(Body Resources),Ultrahuman(Dynamic Recovery),Coros(Daily Stress),and Withings(Health Improvement Score).The most frequently incorporated biometric contributors in this catalogue of CHS were heart rate variability(86%),resting heart rate(79%),physical activity(71%),and sleep duration(71%).However,significant discrepancies were identified in data collection timeframes,metric weighting,and proprietary scoring methodologies.None of the manufacturers disclosed their exact algorithmic formulas,and few provided empirical validation or peer-reviewed evidence supporting the accuracy or clinical relevance of their scores.Summary and outlook:While the concept of CHS represent a promising innovation in digital health,their scientific validity,transparency,and clinical applicability remain uncertain.Future research should focus on establishing standardized sensor fusion frameworks,improving algorithmic transparency,and evaluating CHS across diverse populations.Greater collaboration between industry,researchers,and clinicians is essential to ensure these indices serve as meaningful health metrics rather than opaque consumer tools.展开更多
Smart contracts are self-executing programs on blockchains that manage complex business logic with transparency and integrity.However,their immutability after deployment makes programming errors particularly critical,...Smart contracts are self-executing programs on blockchains that manage complex business logic with transparency and integrity.However,their immutability after deployment makes programming errors particularly critical,as such errors can be exploited to compromise blockchain security.Existing vulnerability detection methods often rely on fixed rules or target specific vulnerabilities,limiting their scalability and adaptability to diverse smart contract scenarios.Furthermore,natural language processing approaches for source code analysis frequently fail to capture program flow,which is essential for identifying structural vulnerabilities.To address these limitations,we propose a novel model that integrates textual and structural information for smart contract vulnerability detection.Our approach employs the CodeBERT NLP model for textual analysis,augmented with structural insights derived from control flow graphs created using the abstract syntax tree and opcode of smart contracts.Each graph node is embedded using Sent2Vec,and centrality analysis is applied to highlight critical paths and nodes within the code.The extracted features are normalized and combined into a prompt for a large language model to detect vulnerabilities effectivel.Experimental results demonstrate the superiority of our model,achieving an accuracy of 86.70%,a recall of 84.87%,a precision of 85.24%,and an F1-score of 84.46%.These outcomes surpass existing methods,including CodeBERT alone(accuracy:81.26%,F1-score:79.84%)and CodeBERT combined with abstract syntax tree analysis(accuracy:83.48%,F1-score:79.65%).The findings underscore the effectiveness of incorporating graph structural information alongside text-based analysis,offering improved scalability and performance in detecting diverse vulnerabilities.展开更多
Innovation in learning algorithms has made retinal vessel segmentation and automatic grading tech-niques crucial for clinical diagnosis and prevention of diabetic retinopathy.The traditional methods struggle with accu...Innovation in learning algorithms has made retinal vessel segmentation and automatic grading tech-niques crucial for clinical diagnosis and prevention of diabetic retinopathy.The traditional methods struggle with accuracy and reliability due to multi-scale variations in retinal blood vessels and the complex pathological relationship in fundus images associated with diabetic retinopathy.While the single-modal diabetic retinopathy grading network addresses class imbalance challenges and lesion representation in fundus image data,dual-modal diabetic retinopathy grading methods offer superior performance.However,the scarcity of dual-modal data and the lack of effective feature fusion methods limit their potential due to multi-scale variations.This paper addresses these issues by focusing on multi-scale retinal vessel segmentation,dual feature fusion,data augmentation,and attention-based grading.The proposed model aims to improve comprehensive segmentation for retinal images with varying vessel thicknesses.It employs a dual-branch parallel architecture that integrates a transformer encoder with a convolutional neural network encoder to extract local and global information for synergistic saliency learning.Besides that,the model uses residual structures and attention modules to extract critical lesions,enhancing the accuracy and reliability of diabetic retinopathy grading.To evaluate the efficacy of the proposed approach,this study compared it with other pre-trained publicly open models,ResNet152V2,ConvNext,Efficient Net,DenseNet,and Swin Transform,with the same developmental parameters.All models achieved approximately 85%accuracy with the same image preparation method.However,the proposed approach outperforms and optimizes existing models by achieving an accuracy of 99.17%,99.04%,and 99.24%,on Kaggle APTOS19,IDRiD,and EyePACS datasets,respectively.These results support the model’s utility in helping ophthalmologists diagnose diabetic retinopathy more rapidly and accurately.展开更多
FeCl_(3) solution is commonly used in the etching process of stainless steel.The typical etching waste liquid contains a significant amount of Fe^(3+),Fe^(2+),Cr^(3+),and Ni^(2+),making it difficult to reuse and posin...FeCl_(3) solution is commonly used in the etching process of stainless steel.The typical etching waste liquid contains a significant amount of Fe^(3+),Fe^(2+),Cr^(3+),and Ni^(2+),making it difficult to reuse and posing pollution issues.The FeCl_(3) etching waste liquid was the present subject,which aimed to extract Cr^(3+)and Ni^(2+)by selectively adjusting process parameters.Additionally,it investigates the migration behavior and phase transition mechanisms of the iron,chromium,and nickel in different solution systems during treatment,systematically elucidating the regeneration mechanisms of FeCl_(3) etching waste liquid.The results indicate that Cr and Ni can be recycled by controlling parameters such as pH value,temperature,and the valence states of the ions.Following a selective reduction of Fe^(3+)to Fe^(2+)using Fe powder,98.3%of Cr^(3+)was recovered by adjusting the solution’s pH.Subsequently,93.3%of Ni^(2+)was extracted from the Cr-depleted solution through further adjustments to the process parameters.The recovered Cr and Ni can be used to prepare Fe–Cr and Fe–Ni alloy powders.Furthermore,the FeCl_(3) etching solution was regenerated by oxidizing Fe^(2+)and recovering impurities.The theoretical support for the development of new processes for treating FeCl_(3) etching waste liquid is provided.展开更多
We explore the feasibility of H I galaxy redshift surveys with the Five-hundred-meter Aperture Spherical Telescope(FAST) and its proposed Core Array interferometry.Using semi-analytical simulations,we compare the perf...We explore the feasibility of H I galaxy redshift surveys with the Five-hundred-meter Aperture Spherical Telescope(FAST) and its proposed Core Array interferometry.Using semi-analytical simulations,we compare the performance of the FAST single-dish and Core Array modes in drift scan (DS) and on-the-fly (OTF) observations across different redshifts.Our results show that the FAST single-dish mode enables significant H I detections at low redshifts (z■0.35) but is limited at higher redshifts due to shot noise.The Core Array interferometry,with higher sensitivity and angular resolution,provides robust H I galaxy detections up to z~1,maintaining a sufficient number density for power spectrum measurements and BAO constraints.At low redshifts (z~0.01–0.08),both configurations perform well,though cosmic variance dominates uncertainties.At higher redshifts (z>0.35),the Core Array outperforms the single-dish mode,while increasing the survey area has little impact on single-dish observations due to shot noise limitations.The DS mode efficiently covers large sky areas but is constrained by Earth’s rotation,whereas the OTF mode allows more flexible deep-field surveys at the cost of operational overhead.Our findings highlight the importance of optimizing survey strategies to maximize FAST’s potential for H I cosmology.The Core Array is particularly well-suited for high-redshift H I galaxy surveys,enabling precise constraints on large-scale structure and dark energy.展开更多
Ischemic stroke(IS)is a prevalent neurological disorder often resulting in significant disability or mortality.Resveratrol,extracted from Polygonum cuspidatum Sieb.et Zucc.(commonly known as Japanese knotweed),has bee...Ischemic stroke(IS)is a prevalent neurological disorder often resulting in significant disability or mortality.Resveratrol,extracted from Polygonum cuspidatum Sieb.et Zucc.(commonly known as Japanese knotweed),has been recognized for its potent neuroprotective properties.However,the neuroprotective efficacy of its derivative,(E)-4-(3,5-dimethoxystyryl)quinoline(RV02),against ischemic stroke remains inadequately explored.This study aimed to evaluate the protective effects of RV02 on neuronal ischemia-reperfusion injury both in vitro and in vivo.The research utilized an animal model of middle cerebral artery occlusion/reperfusion and SH-SY5Y cells subjected to oxygen-glucose deprivation and reperfusion to simulate ischemic conditions.The findings demonstrate that RV02 attenuates neuronal mitochondrial damage and scavenges reactive oxygen species(ROS)through mitophagy activation.Furthermore,Parkin knockdown was found to abolish RV02's ability to activate mitophagy and neuroprotection in vitro.These results suggest that RV02 shows promise as a neuroprotective agent,with the activation of Parkin-mediated mitophagy potentially serving as the primary mechanism underlying its neuroprotective effects.展开更多
As the world’s fourth most populous country,Indonesia presents challenges and opportunities for sustainable energy progress,offering a critical context to investigate green human development(GHD).This study uniquely ...As the world’s fourth most populous country,Indonesia presents challenges and opportunities for sustainable energy progress,offering a critical context to investigate green human development(GHD).This study uniquely contributes to the literature by employing the planetary pressures-adjusted human development index(PHDI)as an indicator of GHD,which integrates environmental impacts into human development.Using static and dynamic econometric methods,including the quantile regression and autoregressive distributed lag model,it explores the impacts of renewable and nonrenewable energy consumption on GHD.The findings demonstrate that renewable energy currently has a detrimental impact on GHD due to its limited adoption and high costs.Conversely,nonrenewable energy positively influences GHD,as it is the primary energy source in the country and is becoming more efficient at reducing emissions.However,the study finds that greater use of renewable energy reduces its adverse effects,suggesting that as renewable energy technologies become more cost-effective and widely implemented,their initial adverse effects could be mitigated,leading to improved long-term GHD outcomes.These findings carry important implications for Indonesia,where the govern‐ment is striving to expand renewable energy capacity while promoting equitable development across its archi‐pelagic regions.They underscore the critical role of energy policy in balancing economic,social,and environmental goals,contributing meaningfully to the country’s sustainable development agenda.展开更多
BACKGROUND The rising incidence of inflammatory bowel disease(IBD)globally has increased disease burden and economic impact.Gaps remain in understanding the IBD burden between Asian and Western populations.AIM To esti...BACKGROUND The rising incidence of inflammatory bowel disease(IBD)globally has increased disease burden and economic impact.Gaps remain in understanding the IBD burden between Asian and Western populations.AIM To estimate the current and following 10-year prevalence and incidence of IBD in Hong Kong,Japan,and the United States.METHODS Patients diagnosed with IBD were identified from a territory-wide electronic medical records database in Hong Kong(2003-2022,including all ages)and two large employment-based healthcare claims databases in Japan and the United States(2010-2022,including<65 age).We used Autoregressive Integrated Moving Average models to predict prevalence and incidence from 2023 to 2032,stratified by disease subtype[ulcerative colitis(UC);Crohn’s disease(CD)],sex,and age,with 95%prediction intervals(PIs).The forecasted annual average percentage change(AAPC)with 95%confidence intervals was calculated.RESULTS The age-standardized prevalence of IBD for 2032 is forecasted at 105.88 per 100000 in Hong Kong(95%PI:83.01-128.75,AAPC:5.85%),645.79 in Japan(95%PI:562.51-741.39,AAPC:5.78%),and 629.85 in the United States(95%PI:569.09-690.63,AAPC:2.85%).Prevalence is estimated to rise most significantly among those under 18 in Japan and the United States.Over the next decade,the incidence of IBD is estimated to increase annually by 3.3%in Hong Kong with forecasted increases across all age groups(although the AAPC for each group is not statistically significant);by 2.88%in Japan with a significant rise in those under 18 and stability in 18-65;and remaining stable in the United States.By 2032,the prevalence of CD is estimated to surpass UC in Hong Kong and the United States,whereas UC will continue to be more prevalent in Japan.A higher prevalence and incidence of IBD is forecast for males in Hong Kong and Japan,whereas rates will be similar for both males and females in the United States.CONCLUSION The prevalence of IBD is forecasted to increase in Hong Kong,Japan,and the United States,while estimates of incidence vary.The forecasts show distinct patterns across disease subtype,sex,and age groups.Health systems will need to plan for the predicted increasing prevalence among different demographics.展开更多
The widespread occurrence of carbapenem-resistant organisms has garnered significant public attention.Arthro-pods,including flies,are important vectors of multidrug-resistant bacteria.In this study,we reported the sim...The widespread occurrence of carbapenem-resistant organisms has garnered significant public attention.Arthro-pods,including flies,are important vectors of multidrug-resistant bacteria.In this study,we reported the simultane-ous carriage of four carbapenem-resistant isolates from different species,namely,Escherichia coli(E.coli),Providencia manganoxydans(P.manganoxydan),Myroides odoratimimus(M.odoratimimus)and Proteus mirabilis(P.mirabilis),from a single fly in China.These isolates were characterized through antimicrobial susceptibility testing,conjuga-tion assays,whole-genome sequencing,and bioinformatics analysis.M.odoratimimus showed intrinsic resistance to carbapenems.The mechanisms of carbapenem resistance in E.coli,P.manganoxydans,and P.mirabilis were due to the production of NDM-5,NDM-1 and NDM-1,respectively.Genetic context of the bla_(NDM) genes in these three isolates varied.The bla_(NDM-5) gene in E.coli was located on an IncHI2/HI2A multidrug-resistant plasmid,which was con-jugatively transferable.The bla_(NDM-1) gene in P.mirabilis resided on the pPM14-NDM_123k-like nonconjugative plasmid.The bla_(NDM-1) gene in P.manganoxydans was found in a nonconjugatively transferable,multidrug-resistant region.The results of this study enhance our understanding of the dissemination of carbapenem-resistant organisms and sug-gest the need for a more comprehensive approach to antibiotic resistance research encompassing humans,animals,and the environment.展开更多
Data volume grows explosively with the proliferation of powerful smartphones and innovative mobile applications.The ability to accurately and extensively monitor and analyze these data is necessary.Much concern in cel...Data volume grows explosively with the proliferation of powerful smartphones and innovative mobile applications.The ability to accurately and extensively monitor and analyze these data is necessary.Much concern in cellular data analysis is related to human beings and their behaviours.Due to the potential value that lies behind these massive data,there have been different proposed approaches for understanding corresponding patterns.To that end,analyzing people's activities,e.g.,counting them at fixed locations and tracking them by generating origindestination matrices is crucial.The former can be used to determine the utilization of assets like roads and city attractions.The latter is valuable when planning transport infrastructure.Such insights allow a government to predict the adoption of new roads,new public transport routes,modification of existing infrastructure,and detection of congestion zones,resulting in more efficient designs and improvement.Smartphone data exploration can help research in various fields,e.g.,urban planning,transportation,health care,and business marketing.It can also help organizations in decision making,policy implementation,monitoring,and evaluation at all levels.This work aims to review the methods and techniques that have been implemented to discover knowledge from mobile phone data.We classify these existing methods and present a taxonomy of the related work by discussing their pros and cons.展开更多
Tourism-led economic growth and tourism-driven urbanization have attracted increasing attention by provinces and regions in China with abundant tourism resources.Due to low data availability,the current tourism litera...Tourism-led economic growth and tourism-driven urbanization have attracted increasing attention by provinces and regions in China with abundant tourism resources.Due to low data availability,the current tourism literature lacks empirical evidence of the tourism network in lessdeveloped mountainous regions where the development of transport infrastructure is more variable.This paper aims to provide such evidence using Guangxi Zhuang Autonomous Region in China as a case study.Using User Generated Content(UGC)data,this study constructs a tourism network in Guangxi.By integrating social network analysis with spatial interaction modelling,we compared the impact of two different transport infrastructures,highway and high-speed railway,on tourist flows,particularly in less-developed mountainous regions.It was found that the product of node centrality and flow could best describe the significant pushing and pulling forces on the flow of tourists.The tourism by high-speed railway was sensitive to the position of trip destination on the whole tourism network but self-drive tourism was more sensitive to travelling time.The increase of high-speed railway density is crucial to promote local tourism-led economic development,however,large-scale karst landforms in the study area present a significant obstacle to the construction of high-speed railways.展开更多
In this age characterized by rapid growth in the volume of data,data deidentification technologies have become crucial in facilitating the analysis of sensitive information.For instance,healthcare information must be ...In this age characterized by rapid growth in the volume of data,data deidentification technologies have become crucial in facilitating the analysis of sensitive information.For instance,healthcare information must be processed through deidentification procedures before being passed to data analysis agencies in order to prevent any exposure of personal details that would violate privacy.As such,privacy protection issues associated with the release of data and data mining have become a popular field of study in the domain of big data.As a strict and verifiable definition of privacy,differential privacy has attracted noteworthy attention and widespread research in recent years.In this study,we analyze the advantages of differential privacy protection mechanisms in comparison to traditional deidentification data protection methods.Furthermore,we examine and analyze the basic theories of differential privacy and relevant studies regarding data release and data mining.展开更多
Prediction of machine failure is challenging as the dataset is often imbalanced with a low failure rate.The common approach to han-dle classification involving imbalanced data is to balance the data using a sampling a...Prediction of machine failure is challenging as the dataset is often imbalanced with a low failure rate.The common approach to han-dle classification involving imbalanced data is to balance the data using a sampling approach such as random undersampling,random oversampling,or Synthetic Minority Oversampling Technique(SMOTE)algorithms.This paper compared the classification performance of three popular classifiers(Logistic Regression,Gaussian Naïve Bayes,and Support Vector Machine)in predicting machine failure in the Oil and Gas industry.The original machine failure dataset consists of 20,473 hourly data and is imbalanced with 19945(97%)‘non-failure’and 528(3%)‘failure data’.The three independent variables to predict machine failure were pressure indicator,flow indicator,and level indicator.The accuracy of the classifiers is very high and close to 100%,but the sensitivity of all classifiers using the original dataset was close to zero.The performance of the three classifiers was then evaluated for data with different imbalance rates(10%to 50%)generated from the original data using SMOTE,SMOTE-Support Vector Machine(SMOTE-SVM)and SMOTE-Edited Nearest Neighbour(SMOTE-ENN).The classifiers were evaluated based on improvement in sensitivity and F-measure.Results showed that the sensitivity of all classifiers increases as the imbalance rate increases.SVM with radial basis function(RBF)kernel has the highest sensitivity when data is balanced(50:50)using SMOTE(Sensitivitytest=0.5686,Ftest=0.6927)compared to Naïve Bayes(Sensitivitytest=0.4033,Ftest=0.6218)and Logistic Regression(Sensitivitytest=0.4194,Ftest=0.621).Overall,the Gaussian Naïve Bayes model consistently improves sensitivity and F-measure as the imbalance ratio increases,but the sensitivity is below 50%.The classifiers performed better when data was balanced using SMOTE-SVM compared to SMOTE and SMOTE-ENN.展开更多
BACKGROUND Oral cancer is the sixth most prevalent cancer worldwide.Public knowledge in oral cancer risk factors and survival is limited.AIM To come up with machine learning(ML)algorithms to predict the length of surv...BACKGROUND Oral cancer is the sixth most prevalent cancer worldwide.Public knowledge in oral cancer risk factors and survival is limited.AIM To come up with machine learning(ML)algorithms to predict the length of survival for individuals diagnosed with oral cancer,and to explore the most important factors that were responsible for shortening or lengthening oral cancer survival.METHODS We used the Surveillance,Epidemiology,and End Results database from the years 1975 to 2016 that consisted of a total of 257880 cases and 94 variables.Four ML techniques in the area of artificial intelligence were applied for model training and validation.Model accuracy was evaluated using mean absolute error(MAE),mean squared error(MSE),root mean squared error(RMSE),R2 and adjusted R2.RESULTS The most important factors predictive of oral cancer survival time were age at diagnosis,primary cancer site,tumor size and year of diagnosis.Year of diagnosis referred to the year when the tumor was first diagnosed,implying that individuals with tumors that were diagnosed in the modern era tend to have longer survival than those diagnosed in the past.The extreme gradient boosting ML algorithms showed the best performance,with the MAE equaled to 13.55,MSE 486.55 and RMSE 22.06.CONCLUSION Using artificial intelligence,we developed a tool that can be used for oral cancer survival prediction and for medical-decision making.The finding relating to the year of diagnosis represented an important new discovery in the literature.The results of this study have implications for cancer prevention and education for the public.展开更多
This paper deals with the recommendation system in the so-called user-centric payment environment where users,i.e.,the payers,can make payments without providing self-information to merchants.This service maintains on...This paper deals with the recommendation system in the so-called user-centric payment environment where users,i.e.,the payers,can make payments without providing self-information to merchants.This service maintains only the minimum purchase information such as the purchased product names,the time of purchase,the place of purchase for possible refunds or cancellations of purchases.This study aims to develop AI-based recommendation system by utilizing the minimum transaction data generated by the user-centric payment service.First,we developed a matrix-based extrapolative collaborative filtering algorithm based on open transaction data.The recommendation methodology was verified with the real transaction data.Based on the experimental results,we confirmed that the recommendation performance is satisfactory only with the minimum purchase information.展开更多
The race to develop the next generation of wireless networks,known as Sixth Generation(6G)wireless,which will be operational in 2030,has already begun.To realize its full potential over the next decade,6G will undoubt...The race to develop the next generation of wireless networks,known as Sixth Generation(6G)wireless,which will be operational in 2030,has already begun.To realize its full potential over the next decade,6G will undoubtedly necessitate additional improvements that integrate existing solutions with cutting-edge ones.However,the studies about 6G are mainly limited and scattered,whereas no bibliometric study covers the 6G field.Thus,this study aims to review,examine,and summarize existing studies and research activities in 6G.This study has examined the Scopus database through a bibliometric analysis of more than 1,000 papers published between 2017 and 2021.Then,we applied the bibliometric analysis methods by including(1)document type,(2)subject area,(3)author,and(4)country of publication.The study’s results reflect the research 6G community’s trends,highlight important research challenges,and elucidate potential directions for future research in this interesting area.展开更多
基金This work is supported by the Major International Joint Research Project of the National Natural Science Foundation of China(Grant No.71520107004)the Major Program of National Natural Science Foundation of China(Grant No.71790614)+1 种基金the Fund for Innovative Research Groups of the National Natural Science Foundation of China(Grant No.71621061)and the 111 Project(Grant No.B16009).
文摘Industrial intelligence is a core technology in the upgrading of the production processes and management modes of traditional industries.Motivated by the major development strategies and needs of industrial intellectualization in China,this study presents an innovative fusion structure that encompasses the theoretical foundation and technological innovation of data analytics and optimization,as well as their application to smart industrial engineering.First,this study describes a general methodology for the fusion of data analytics and optimization.Then,it identifies some data analytics and system optimization technologies to handle key issues in smart manufacturing.Finally,it provides a four-level framework for smart industry based on the theoretical and technological research on the fusion of data analytics and optimization.The framework uses data analytics to perceive and analyze industrial production and logistics processes.It also demonstrates the intelligent capability of planning,scheduling,operation optimization,and optimal control.Data analytics and system optimization technologies are employed in the four-level framework to overcome some critical issues commonly faced by manufacturing,resources and materials,energy,and logistics systems,such as high energy consumption,high costs,low energy efficiency,low resource utilization,and serious environmental pollution.The fusion of data analytics and optimization allows enterprises to enhance the prediction and control of unknown areas and discover hidden knowledge to improve decision-making efficiency。Therefore,industrial intelligence has great importance in China’s industrial upgrading and transformation into a true industrial power.
基金supported by the National Natural Science Foundation of China under Grant Nos.12305069,11947022,12473001,11975072,11875102,and 11835009the National SKA Program of China under Grants Nos.2022SKA0110200 and 2022SKA0110203+1 种基金the Program of the Education Department of Liaoning Province under Grant No.JYTMS20231695the National 111 Project under Grant No.B16009。
文摘Sterile neutrinos can influence the evolution of the Universe,and thus cosmological observations can be used to detect them.Future gravitational-wave(GW)observations can precisely measure absolute cosmological distances,helping to break parameter degeneracies generated by traditional cosmological observations.This advancement can lead to much tighter constraints on sterile neutrino parameters.This work provides a preliminary forecast for detecting sterile neutrinos using third-generation GW detectors in combination with future shortγ-ray burst observations from a THESEUS-like telescope,an approach not previously explored in the literature.Both massless and massive sterile neutrinos are considered within theΛCDM cosmology.We find that using GW data can greatly enhance the detection capability for massless sterile neutrinos,reaching 3σlevel.For massive sterile neutrinos,GW data can also greatly assist in improving the parameter constraints,but it seems that effective detection is still not feasible.
基金funded by Big Data Analytics Centre(BIDAC)of United Arab Emirates University under the grant numbers G00003679 and G00004526。
文摘In this paper,we explore the ever-changing field ofDigital Twins(DT)in the Industrial Internet of Things(IIoT)context,emphasizing their critical role in advancing Industry 4.0 toward the frontiers of Industry 5.0.The article explores the applications of DT in several industrial sectors and their smooth integration into the IIoT,focusing on the fundamentals of digital twins and emphasizing the importance of virtual-real integration.It discusses the emergence of DT,contextualizing its evolution within the framework of IIoT.The study categorizes the different types of DT,including prototypes and instances,and provides an in-depth analysis of the enabling technologies such as IoT,Artificial Intelligence(AI),Extended Reality(XR),cloud computing,and the Application Programming Interface(API).The paper demonstrates theDT advantages through the practical integration of real-world case studies,which highlights the technology’s exceptional capacity to improve traceability and fault detection within the context of the IIoT.This paper offers a focused,application-driven perspective on DTs in IIoT,specifically highlighting their role in key production phases such as designing,intelligent manufacturing,maintenance,resource management,automation,security,and safety.By emphasizing their potential to support human-centric,sustainable advancements in Industry 5.0,this study distinguishes itself from existing literature.It provides valuable insights that connect theoretical advancements with practical implementation,making it a crucial resource for researchers,practitioners,and industry professionals.
文摘Objective To explore potential keywords,research clusters,collaborative pattern,and research trends in the field of medical technology management(MTM)through bibliometric analysis,providing insights for researchers,policy makers,and hospital administrators.Methods A retrieval formula was applied to the title,abstract,and keywords in the Web of Science(WoS)Core Collection,along with system-recommended terms,to identify articles on MTM.A total of 181 articles published between 1974 and 2022 were retained for quantitative analysis.The global trend of research output;total citations,average citations,and H-index;and bibliographic coupling,co-authorship,and keyword co-occurrence were analyzed using VOSviewer.Results The number of articles on MTM has been steadily increasing year by year.The focus of research has shifted from addressing basic medical needs to prioritizing emergency response and medical information security.The United States,Italy,and the United Kingdom emerged as the main contributors,with the United States leading in both volume of publications(60 articles)and academic impact(H-index=21).Authors from the United Kingdom and the United States led the way in cross-border cooperation.The top five institutions,ranked by total link strength among cross-institutional authors,were primarily located in Canada and Spain.Conclusions The field of MTM has experienced stable growth over the past three decades(1993-2022).The shift of research focus has prompted a heightened emphasis on protecting patient privacy and ensuring the security of medical data.Future research should emphasize interdisciplinary and professional collaboration,as well as international cooperation and open sharing of knowledge.
基金funded by the Health Research Board in Ireland(Grant ID:HRB ILP-PHR-2024-005)Research Ireland(Grant ID:12/RC/2289_P2).
文摘Introduction:Consumer wearables increasingly provide users with Composite Health Scores(CHS)–integrated biometric indices that claim to quantify readiness,recovery,stress,or overall well-being.Despite their growing adoption,the validity,transparency,and physiological relevance of these scores remain unclear.This study systematically evaluates CHS fromleading wearablemanufacturers to assess their underlying methodologies,contributors,and scientific basis.Content:Information was synthesised from publicly available company documentation,including technical white papers,user manuals,app interfaces,and research literature where available.We identified 14 CHS across 10 major wearable manufacturers,including Fitbit(Daily Readiness),Garmin(Body Battery^(TM)and Training Readiness),Oura(Readiness and Resilience),WHOOP(Strain,Recovery,and Stress Monitor),Polar(Nightly Recharge^(TM)),Samsung(Energy Score),Suunto(Body Resources),Ultrahuman(Dynamic Recovery),Coros(Daily Stress),and Withings(Health Improvement Score).The most frequently incorporated biometric contributors in this catalogue of CHS were heart rate variability(86%),resting heart rate(79%),physical activity(71%),and sleep duration(71%).However,significant discrepancies were identified in data collection timeframes,metric weighting,and proprietary scoring methodologies.None of the manufacturers disclosed their exact algorithmic formulas,and few provided empirical validation or peer-reviewed evidence supporting the accuracy or clinical relevance of their scores.Summary and outlook:While the concept of CHS represent a promising innovation in digital health,their scientific validity,transparency,and clinical applicability remain uncertain.Future research should focus on establishing standardized sensor fusion frameworks,improving algorithmic transparency,and evaluating CHS across diverse populations.Greater collaboration between industry,researchers,and clinicians is essential to ensure these indices serve as meaningful health metrics rather than opaque consumer tools.
基金supported by the Seoul Business Agency(SBA),funded by the Seoul Metropolitan Government,through the Seoul R&BD Program(FB240022)by the Korea Institute for Advancement of Technology(KIAT),funded by the Korea Government(MOTIE)(RS-2024-00406796)+1 种基金through the HRD Program for Industrial Innovationby the Excellent Researcher Support Project of Kwangwoon University in 2024.
文摘Smart contracts are self-executing programs on blockchains that manage complex business logic with transparency and integrity.However,their immutability after deployment makes programming errors particularly critical,as such errors can be exploited to compromise blockchain security.Existing vulnerability detection methods often rely on fixed rules or target specific vulnerabilities,limiting their scalability and adaptability to diverse smart contract scenarios.Furthermore,natural language processing approaches for source code analysis frequently fail to capture program flow,which is essential for identifying structural vulnerabilities.To address these limitations,we propose a novel model that integrates textual and structural information for smart contract vulnerability detection.Our approach employs the CodeBERT NLP model for textual analysis,augmented with structural insights derived from control flow graphs created using the abstract syntax tree and opcode of smart contracts.Each graph node is embedded using Sent2Vec,and centrality analysis is applied to highlight critical paths and nodes within the code.The extracted features are normalized and combined into a prompt for a large language model to detect vulnerabilities effectivel.Experimental results demonstrate the superiority of our model,achieving an accuracy of 86.70%,a recall of 84.87%,a precision of 85.24%,and an F1-score of 84.46%.These outcomes surpass existing methods,including CodeBERT alone(accuracy:81.26%,F1-score:79.84%)and CodeBERT combined with abstract syntax tree analysis(accuracy:83.48%,F1-score:79.65%).The findings underscore the effectiveness of incorporating graph structural information alongside text-based analysis,offering improved scalability and performance in detecting diverse vulnerabilities.
基金funded by Princess Nourah bint Abdulrahman University and Researchers Supporting Project number(PNURSP2025R346)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Innovation in learning algorithms has made retinal vessel segmentation and automatic grading tech-niques crucial for clinical diagnosis and prevention of diabetic retinopathy.The traditional methods struggle with accuracy and reliability due to multi-scale variations in retinal blood vessels and the complex pathological relationship in fundus images associated with diabetic retinopathy.While the single-modal diabetic retinopathy grading network addresses class imbalance challenges and lesion representation in fundus image data,dual-modal diabetic retinopathy grading methods offer superior performance.However,the scarcity of dual-modal data and the lack of effective feature fusion methods limit their potential due to multi-scale variations.This paper addresses these issues by focusing on multi-scale retinal vessel segmentation,dual feature fusion,data augmentation,and attention-based grading.The proposed model aims to improve comprehensive segmentation for retinal images with varying vessel thicknesses.It employs a dual-branch parallel architecture that integrates a transformer encoder with a convolutional neural network encoder to extract local and global information for synergistic saliency learning.Besides that,the model uses residual structures and attention modules to extract critical lesions,enhancing the accuracy and reliability of diabetic retinopathy grading.To evaluate the efficacy of the proposed approach,this study compared it with other pre-trained publicly open models,ResNet152V2,ConvNext,Efficient Net,DenseNet,and Swin Transform,with the same developmental parameters.All models achieved approximately 85%accuracy with the same image preparation method.However,the proposed approach outperforms and optimizes existing models by achieving an accuracy of 99.17%,99.04%,and 99.24%,on Kaggle APTOS19,IDRiD,and EyePACS datasets,respectively.These results support the model’s utility in helping ophthalmologists diagnose diabetic retinopathy more rapidly and accurately.
基金financially supported by the National Natural Science Foundation of China(Nos.52074078 and 52374327)the Applied Fundamental Research Program of Liaoning Province(No.2023JH2/101600002)+5 种基金the Liaoning Provincial Natural Science Foundation of China(No.2022-YQ-09)the Shenyang Young Middle-Aged Scientific and Technological Innovation Talent Support Program,China(No.RC220491)the Liaoning Province Steel Industry-University-Research Innovation Alliance Cooperation Project of Bensteel Group,China(No.KJBLM202202)the Fundamental Research Funds for the Central Universities,China(Nos.N2201023 and N2325009)the Key Scientific Research Project of Liaoning Provincial Department of Education(2024JYTZD-03)the 111 Project(B16009).
文摘FeCl_(3) solution is commonly used in the etching process of stainless steel.The typical etching waste liquid contains a significant amount of Fe^(3+),Fe^(2+),Cr^(3+),and Ni^(2+),making it difficult to reuse and posing pollution issues.The FeCl_(3) etching waste liquid was the present subject,which aimed to extract Cr^(3+)and Ni^(2+)by selectively adjusting process parameters.Additionally,it investigates the migration behavior and phase transition mechanisms of the iron,chromium,and nickel in different solution systems during treatment,systematically elucidating the regeneration mechanisms of FeCl_(3) etching waste liquid.The results indicate that Cr and Ni can be recycled by controlling parameters such as pH value,temperature,and the valence states of the ions.Following a selective reduction of Fe^(3+)to Fe^(2+)using Fe powder,98.3%of Cr^(3+)was recovered by adjusting the solution’s pH.Subsequently,93.3%of Ni^(2+)was extracted from the Cr-depleted solution through further adjustments to the process parameters.The recovered Cr and Ni can be used to prepare Fe–Cr and Fe–Ni alloy powders.Furthermore,the FeCl_(3) etching solution was regenerated by oxidizing Fe^(2+)and recovering impurities.The theoretical support for the development of new processes for treating FeCl_(3) etching waste liquid is provided.
基金the support of the National SKA Program of China (Nos.2022SKA0110200 and 2022SKA0110203)the National Natural Science Foundation of China (NSFC,Nos.12473091 and 12473001),and 111 Project (No.B16009)the support of the Fundamental Research Funds for the Central Universities (No.N2405008)。
文摘We explore the feasibility of H I galaxy redshift surveys with the Five-hundred-meter Aperture Spherical Telescope(FAST) and its proposed Core Array interferometry.Using semi-analytical simulations,we compare the performance of the FAST single-dish and Core Array modes in drift scan (DS) and on-the-fly (OTF) observations across different redshifts.Our results show that the FAST single-dish mode enables significant H I detections at low redshifts (z■0.35) but is limited at higher redshifts due to shot noise.The Core Array interferometry,with higher sensitivity and angular resolution,provides robust H I galaxy detections up to z~1,maintaining a sufficient number density for power spectrum measurements and BAO constraints.At low redshifts (z~0.01–0.08),both configurations perform well,though cosmic variance dominates uncertainties.At higher redshifts (z>0.35),the Core Array outperforms the single-dish mode,while increasing the survey area has little impact on single-dish observations due to shot noise limitations.The DS mode efficiently covers large sky areas but is constrained by Earth’s rotation,whereas the OTF mode allows more flexible deep-field surveys at the cost of operational overhead.Our findings highlight the importance of optimizing survey strategies to maximize FAST’s potential for H I cosmology.The Core Array is particularly well-suited for high-redshift H I galaxy surveys,enabling precise constraints on large-scale structure and dark energy.
基金supported by the National Natural ScienceFoundation of China (No.82174076)the Construction Project of Liaoning Provincial Key Laboratory,China (No.2022JH13/10200026)+2 种基金the Fundamental Research Funds for the Central Universities (No.N2220002)the 111 Project (No.B16009)the Research Project of Educational Commission of Liaoning Province (No.LJ212410164003)。
文摘Ischemic stroke(IS)is a prevalent neurological disorder often resulting in significant disability or mortality.Resveratrol,extracted from Polygonum cuspidatum Sieb.et Zucc.(commonly known as Japanese knotweed),has been recognized for its potent neuroprotective properties.However,the neuroprotective efficacy of its derivative,(E)-4-(3,5-dimethoxystyryl)quinoline(RV02),against ischemic stroke remains inadequately explored.This study aimed to evaluate the protective effects of RV02 on neuronal ischemia-reperfusion injury both in vitro and in vivo.The research utilized an animal model of middle cerebral artery occlusion/reperfusion and SH-SY5Y cells subjected to oxygen-glucose deprivation and reperfusion to simulate ischemic conditions.The findings demonstrate that RV02 attenuates neuronal mitochondrial damage and scavenges reactive oxygen species(ROS)through mitophagy activation.Furthermore,Parkin knockdown was found to abolish RV02's ability to activate mitophagy and neuroprotection in vitro.These results suggest that RV02 shows promise as a neuroprotective agent,with the activation of Parkin-mediated mitophagy potentially serving as the primary mechanism underlying its neuroprotective effects.
文摘As the world’s fourth most populous country,Indonesia presents challenges and opportunities for sustainable energy progress,offering a critical context to investigate green human development(GHD).This study uniquely contributes to the literature by employing the planetary pressures-adjusted human development index(PHDI)as an indicator of GHD,which integrates environmental impacts into human development.Using static and dynamic econometric methods,including the quantile regression and autoregressive distributed lag model,it explores the impacts of renewable and nonrenewable energy consumption on GHD.The findings demonstrate that renewable energy currently has a detrimental impact on GHD due to its limited adoption and high costs.Conversely,nonrenewable energy positively influences GHD,as it is the primary energy source in the country and is becoming more efficient at reducing emissions.However,the study finds that greater use of renewable energy reduces its adverse effects,suggesting that as renewable energy technologies become more cost-effective and widely implemented,their initial adverse effects could be mitigated,leading to improved long-term GHD outcomes.These findings carry important implications for Indonesia,where the govern‐ment is striving to expand renewable energy capacity while promoting equitable development across its archi‐pelagic regions.They underscore the critical role of energy policy in balancing economic,social,and environmental goals,contributing meaningfully to the country’s sustainable development agenda.
基金Supported by the Research Grant Council,Research Impact Fund,No.R7007-22.
文摘BACKGROUND The rising incidence of inflammatory bowel disease(IBD)globally has increased disease burden and economic impact.Gaps remain in understanding the IBD burden between Asian and Western populations.AIM To estimate the current and following 10-year prevalence and incidence of IBD in Hong Kong,Japan,and the United States.METHODS Patients diagnosed with IBD were identified from a territory-wide electronic medical records database in Hong Kong(2003-2022,including all ages)and two large employment-based healthcare claims databases in Japan and the United States(2010-2022,including<65 age).We used Autoregressive Integrated Moving Average models to predict prevalence and incidence from 2023 to 2032,stratified by disease subtype[ulcerative colitis(UC);Crohn’s disease(CD)],sex,and age,with 95%prediction intervals(PIs).The forecasted annual average percentage change(AAPC)with 95%confidence intervals was calculated.RESULTS The age-standardized prevalence of IBD for 2032 is forecasted at 105.88 per 100000 in Hong Kong(95%PI:83.01-128.75,AAPC:5.85%),645.79 in Japan(95%PI:562.51-741.39,AAPC:5.78%),and 629.85 in the United States(95%PI:569.09-690.63,AAPC:2.85%).Prevalence is estimated to rise most significantly among those under 18 in Japan and the United States.Over the next decade,the incidence of IBD is estimated to increase annually by 3.3%in Hong Kong with forecasted increases across all age groups(although the AAPC for each group is not statistically significant);by 2.88%in Japan with a significant rise in those under 18 and stability in 18-65;and remaining stable in the United States.By 2032,the prevalence of CD is estimated to surpass UC in Hong Kong and the United States,whereas UC will continue to be more prevalent in Japan.A higher prevalence and incidence of IBD is forecast for males in Hong Kong and Japan,whereas rates will be similar for both males and females in the United States.CONCLUSION The prevalence of IBD is forecasted to increase in Hong Kong,Japan,and the United States,while estimates of incidence vary.The forecasts show distinct patterns across disease subtype,sex,and age groups.Health systems will need to plan for the predicted increasing prevalence among different demographics.
基金funded by the National Natural Science Foundation of China(Grant No.32300156)the Natural Science Foundation of Jiangsu Province(Grant No.BK20220493).
文摘The widespread occurrence of carbapenem-resistant organisms has garnered significant public attention.Arthro-pods,including flies,are important vectors of multidrug-resistant bacteria.In this study,we reported the simultane-ous carriage of four carbapenem-resistant isolates from different species,namely,Escherichia coli(E.coli),Providencia manganoxydans(P.manganoxydan),Myroides odoratimimus(M.odoratimimus)and Proteus mirabilis(P.mirabilis),from a single fly in China.These isolates were characterized through antimicrobial susceptibility testing,conjuga-tion assays,whole-genome sequencing,and bioinformatics analysis.M.odoratimimus showed intrinsic resistance to carbapenems.The mechanisms of carbapenem resistance in E.coli,P.manganoxydans,and P.mirabilis were due to the production of NDM-5,NDM-1 and NDM-1,respectively.Genetic context of the bla_(NDM) genes in these three isolates varied.The bla_(NDM-5) gene in E.coli was located on an IncHI2/HI2A multidrug-resistant plasmid,which was con-jugatively transferable.The bla_(NDM-1) gene in P.mirabilis resided on the pPM14-NDM_123k-like nonconjugative plasmid.The bla_(NDM-1) gene in P.manganoxydans was found in a nonconjugatively transferable,multidrug-resistant region.The results of this study enhance our understanding of the dissemination of carbapenem-resistant organisms and sug-gest the need for a more comprehensive approach to antibiotic resistance research encompassing humans,animals,and the environment.
基金supported by Fundo para o Desenvolvimento das Ciencias e da Tecnologia(FDCT)(119/2014/A3)。
文摘Data volume grows explosively with the proliferation of powerful smartphones and innovative mobile applications.The ability to accurately and extensively monitor and analyze these data is necessary.Much concern in cellular data analysis is related to human beings and their behaviours.Due to the potential value that lies behind these massive data,there have been different proposed approaches for understanding corresponding patterns.To that end,analyzing people's activities,e.g.,counting them at fixed locations and tracking them by generating origindestination matrices is crucial.The former can be used to determine the utilization of assets like roads and city attractions.The latter is valuable when planning transport infrastructure.Such insights allow a government to predict the adoption of new roads,new public transport routes,modification of existing infrastructure,and detection of congestion zones,resulting in more efficient designs and improvement.Smartphone data exploration can help research in various fields,e.g.,urban planning,transportation,health care,and business marketing.It can also help organizations in decision making,policy implementation,monitoring,and evaluation at all levels.This work aims to review the methods and techniques that have been implemented to discover knowledge from mobile phone data.We classify these existing methods and present a taxonomy of the related work by discussing their pros and cons.
基金funded by the Guangxi Natural Science Foundation(Grant No.2020GXNSFAA159065)the Opening Fund of Key Laboratory of Environment Change and Resources Use in Beibu Gulf under Ministry of Education(Nanning Normal University)+1 种基金Guangxi Key Laboratory of Earth Surface Processes and Intelligent Simulation(Nanning Normal University)(Grant No.GTEU-KLOP-K1701)the seventh batch of distinguished experts in Guangxi and National Natural Science Foundation of China(Grant No.41867071)。
文摘Tourism-led economic growth and tourism-driven urbanization have attracted increasing attention by provinces and regions in China with abundant tourism resources.Due to low data availability,the current tourism literature lacks empirical evidence of the tourism network in lessdeveloped mountainous regions where the development of transport infrastructure is more variable.This paper aims to provide such evidence using Guangxi Zhuang Autonomous Region in China as a case study.Using User Generated Content(UGC)data,this study constructs a tourism network in Guangxi.By integrating social network analysis with spatial interaction modelling,we compared the impact of two different transport infrastructures,highway and high-speed railway,on tourist flows,particularly in less-developed mountainous regions.It was found that the product of node centrality and flow could best describe the significant pushing and pulling forces on the flow of tourists.The tourism by high-speed railway was sensitive to the position of trip destination on the whole tourism network but self-drive tourism was more sensitive to travelling time.The increase of high-speed railway density is crucial to promote local tourism-led economic development,however,large-scale karst landforms in the study area present a significant obstacle to the construction of high-speed railways.
基金supported by the “Ⅲ Innovative and Prospective Technologies Project(1/1)” of the Institute for Information Industry
文摘In this age characterized by rapid growth in the volume of data,data deidentification technologies have become crucial in facilitating the analysis of sensitive information.For instance,healthcare information must be processed through deidentification procedures before being passed to data analysis agencies in order to prevent any exposure of personal details that would violate privacy.As such,privacy protection issues associated with the release of data and data mining have become a popular field of study in the domain of big data.As a strict and verifiable definition of privacy,differential privacy has attracted noteworthy attention and widespread research in recent years.In this study,we analyze the advantages of differential privacy protection mechanisms in comparison to traditional deidentification data protection methods.Furthermore,we examine and analyze the basic theories of differential privacy and relevant studies regarding data release and data mining.
基金supported under the research Grant(PO Number:920138936)from the Institute of Technology PETRONAS Sdn Bhd,32610,Bandar Seri Iskandar,Perak,Malaysia.
文摘Prediction of machine failure is challenging as the dataset is often imbalanced with a low failure rate.The common approach to han-dle classification involving imbalanced data is to balance the data using a sampling approach such as random undersampling,random oversampling,or Synthetic Minority Oversampling Technique(SMOTE)algorithms.This paper compared the classification performance of three popular classifiers(Logistic Regression,Gaussian Naïve Bayes,and Support Vector Machine)in predicting machine failure in the Oil and Gas industry.The original machine failure dataset consists of 20,473 hourly data and is imbalanced with 19945(97%)‘non-failure’and 528(3%)‘failure data’.The three independent variables to predict machine failure were pressure indicator,flow indicator,and level indicator.The accuracy of the classifiers is very high and close to 100%,but the sensitivity of all classifiers using the original dataset was close to zero.The performance of the three classifiers was then evaluated for data with different imbalance rates(10%to 50%)generated from the original data using SMOTE,SMOTE-Support Vector Machine(SMOTE-SVM)and SMOTE-Edited Nearest Neighbour(SMOTE-ENN).The classifiers were evaluated based on improvement in sensitivity and F-measure.Results showed that the sensitivity of all classifiers increases as the imbalance rate increases.SVM with radial basis function(RBF)kernel has the highest sensitivity when data is balanced(50:50)using SMOTE(Sensitivitytest=0.5686,Ftest=0.6927)compared to Naïve Bayes(Sensitivitytest=0.4033,Ftest=0.6218)and Logistic Regression(Sensitivitytest=0.4194,Ftest=0.621).Overall,the Gaussian Naïve Bayes model consistently improves sensitivity and F-measure as the imbalance ratio increases,but the sensitivity is below 50%.The classifiers performed better when data was balanced using SMOTE-SVM compared to SMOTE and SMOTE-ENN.
基金The authors sincerely thank the Clinical Outcomes Research and Education at Collegeof Dental Medicine, Roseman University of Health Sciences for supporting this study.
文摘BACKGROUND Oral cancer is the sixth most prevalent cancer worldwide.Public knowledge in oral cancer risk factors and survival is limited.AIM To come up with machine learning(ML)algorithms to predict the length of survival for individuals diagnosed with oral cancer,and to explore the most important factors that were responsible for shortening or lengthening oral cancer survival.METHODS We used the Surveillance,Epidemiology,and End Results database from the years 1975 to 2016 that consisted of a total of 257880 cases and 94 variables.Four ML techniques in the area of artificial intelligence were applied for model training and validation.Model accuracy was evaluated using mean absolute error(MAE),mean squared error(MSE),root mean squared error(RMSE),R2 and adjusted R2.RESULTS The most important factors predictive of oral cancer survival time were age at diagnosis,primary cancer site,tumor size and year of diagnosis.Year of diagnosis referred to the year when the tumor was first diagnosed,implying that individuals with tumors that were diagnosed in the modern era tend to have longer survival than those diagnosed in the past.The extreme gradient boosting ML algorithms showed the best performance,with the MAE equaled to 13.55,MSE 486.55 and RMSE 22.06.CONCLUSION Using artificial intelligence,we developed a tool that can be used for oral cancer survival prediction and for medical-decision making.The finding relating to the year of diagnosis represented an important new discovery in the literature.The results of this study have implications for cancer prevention and education for the public.
基金supported under the framework of international cooperation program managed by the National Research Foundation of Korea(NRF 2020K2A9A2A06069972,FY2020)supported by the BK21 FOUR(Fostering Outstanding Universities for Research)funded by the Ministry of Education of the Republic of Korea and National Research Foundation of Korea(NRF)supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea(NRF-2020S1A5B8103855).
文摘This paper deals with the recommendation system in the so-called user-centric payment environment where users,i.e.,the payers,can make payments without providing self-information to merchants.This service maintains only the minimum purchase information such as the purchased product names,the time of purchase,the place of purchase for possible refunds or cancellations of purchases.This study aims to develop AI-based recommendation system by utilizing the minimum transaction data generated by the user-centric payment service.First,we developed a matrix-based extrapolative collaborative filtering algorithm based on open transaction data.The recommendation methodology was verified with the real transaction data.Based on the experimental results,we confirmed that the recommendation performance is satisfactory only with the minimum purchase information.
基金The authors received Universiti Malaysia Pahang Al-Sultan Abdullah(UMPSA)grant under Internal Research Grant with Grant Number PDU223209.Author received grant is:Ahmad Firdaus Website of the sponsor:https://www.ump.edu.my/en.
文摘The race to develop the next generation of wireless networks,known as Sixth Generation(6G)wireless,which will be operational in 2030,has already begun.To realize its full potential over the next decade,6G will undoubtedly necessitate additional improvements that integrate existing solutions with cutting-edge ones.However,the studies about 6G are mainly limited and scattered,whereas no bibliometric study covers the 6G field.Thus,this study aims to review,examine,and summarize existing studies and research activities in 6G.This study has examined the Scopus database through a bibliometric analysis of more than 1,000 papers published between 2017 and 2021.Then,we applied the bibliometric analysis methods by including(1)document type,(2)subject area,(3)author,and(4)country of publication.The study’s results reflect the research 6G community’s trends,highlight important research challenges,and elucidate potential directions for future research in this interesting area.
