The widespread usage of rechargeable batteries in portable devices,electric vehicles,and energy storage systems has underscored the importance for accurately predicting their lifetimes.However,data scarcity often limi...The widespread usage of rechargeable batteries in portable devices,electric vehicles,and energy storage systems has underscored the importance for accurately predicting their lifetimes.However,data scarcity often limits the accuracy of prediction models,which is escalated by the incompletion of data induced by the issues such as sensor failures.To address these challenges,we propose a novel approach to accommodate data insufficiency through achieving external information from incomplete data samples,which are usually discarded in existing studies.In order to fully unleash the prediction power of incomplete data,we have investigated the Multiple Imputation by Chained Equations(MICE)method that diversifies the training data through exploring the potential data patterns.The experimental results demonstrate that the proposed method significantly outperforms the baselines in the most considered scenarios while reducing the prediction root mean square error(RMSE)by up to 18.9%.Furthermore,we have also observed that the penetration of incomplete data benefits the explainability of the prediction model through facilitating the feature selection.展开更多
Complex network models are frequently employed for simulating and studyingdiverse real-world complex systems.Among these models,scale-free networks typically exhibit greater fragility to malicious attacks.Consequently...Complex network models are frequently employed for simulating and studyingdiverse real-world complex systems.Among these models,scale-free networks typically exhibit greater fragility to malicious attacks.Consequently,enhancing the robustness of scale-free networks has become a pressing issue.To address this problem,this paper proposes a Multi-Granularity Integration Algorithm(MGIA),which aims to improve the robustness of scale-free networks while keeping the initial degree of each node unchanged,ensuring network connectivity and avoiding the generation of multiple edges.The algorithm generates a multi-granularity structure from the initial network to be optimized,then uses different optimization strategies to optimize the networks at various granular layers in this structure,and finally realizes the information exchange between different granular layers,thereby further enhancing the optimization effect.We propose new network refresh,crossover,and mutation operators to ensure that the optimized network satisfies the given constraints.Meanwhile,we propose new network similarity and network dissimilarity evaluation metrics to improve the effectiveness of the optimization operators in the algorithm.In the experiments,the MGIA enhances the robustness of the scale-free network by 67.6%.This improvement is approximately 17.2%higher than the optimization effects achieved by eight currently existing complex network robustness optimization algorithms.展开更多
This study presents a comprehensive and secure architectural framework for the Internet of Medical Things(IoMT),integrating the foundational principles of the Confidentiality,Integrity,and Availability(CIA)triad along...This study presents a comprehensive and secure architectural framework for the Internet of Medical Things(IoMT),integrating the foundational principles of the Confidentiality,Integrity,and Availability(CIA)triad along with authentication mechanisms.Leveraging advanced Machine Learning(ML)and Deep Learning(DL)techniques,the proposed system is designed to safeguard Patient-Generated Health Data(PGHD)across interconnected medical devices.Given the increasing complexity and scale of cyber threats in IoMT environments,the integration of Intrusion Detection and Prevention Systems(IDPS)with intelligent analytics is critical.Our methodology employs both standalone and hybrid ML&DL models to automate threat detection and enable real-time analysis,while ensuring rapid and accurate responses to a diverse array of attacks.Emphasis is placed on systematic model evaluation using detection metrics such as accuracy,False Alarm Rate(FAR),and False Discovery Rate(FDR),with performance validation through cross-validation and statistical significance testing.Experimental results based on the Edge-IIoTset dataset demonstrate the superior performance of ensemble-based ML models such as Extreme Gradient Boosting(XGB)and hybrid DL models such as Convolutional Neural Networks with Autoencoders(CNN+AE),which achieved detection accuracies of 96%and 98%,respectively,with notably low FARs.These findings underscore the effectiveness of combining traditional security principles with advanced AI-driven methodologies to ensure secure,resilient,and trustworthy healthcare systems within the IoMT ecosystem.展开更多
Identifying vital nodes is one of the core issues of network science,and is crucial for epidemic prevention and control,network security maintenance,and biomedical research and development.In this paper,a new vital no...Identifying vital nodes is one of the core issues of network science,and is crucial for epidemic prevention and control,network security maintenance,and biomedical research and development.In this paper,a new vital nodes identification method,named degree and cycle ratio(DC),is proposed by integrating degree centrality(weightα)and cycle ratio(weight 1-α).The results show that the dynamic observations and weightαare nonlinear and non-monotonicity(i.e.,there exists an optimal valueα^(*)forα),and that DC performs better than a single index in most networks.According to the value ofα^(*),networks are classified into degree-dominant networks(α^(*)>0.5)and cycle-dominant networks(α^(*)<0.5).Specifically,in most degree-dominant networks(such as Chengdu-BUS,Chongqing-BUS and Beijing-BUS),degree is dominant in the identification of vital nodes,but the identification effect can be improved by adding cycle structure information to the nodes.In most cycle-dominant networks(such as Email,Wiki and Hamsterster),the cycle ratio is dominant in the identification of vital nodes,but the effect can be notably enhanced by additional node degree information.Finally,interestingly,in Lancichinetti-Fortunato-Radicchi(LFR)synthesis networks,the cycle-dominant network is observed.展开更多
BACKGROUND Postoperative pulmonary complications(PPCs)are the most common complications following major upper abdominal surgeries,particularly hepatobiliary procedures,and significantly compromise surgical outcomes an...BACKGROUND Postoperative pulmonary complications(PPCs)are the most common complications following major upper abdominal surgeries,particularly hepatobiliary procedures,and significantly compromise surgical outcomes and patients’quality of life.Although the adoption of laparoscopy has lowered their incidence,PPCs remain a frequent and serious concern after hepatobiliary surgery.Existing research on risk factors specific to hepatobiliary surgeries is limited,particularly regarding the epidemiology and risk factors of PPCs in liver and gallbladder surgeries in China.Therefore,this study aimed to investigate the risk factors for PPCs in a large hepatobiliary center.AIM To identify the incidence and risk factors for PPCs following hepatobiliary surgery based on perioperative variables.METHODS Retrospective data were collected from patients who underwent liver,gallbladder,or pancreatic surgery at a hepatobiliary center in China between May 2023 and December 2023.We retrospectively reviewed comprehensive medical records to extract demographic and hospital admission information for determining PPC incidence.Statistically significant variables were initially screened through univariate analysis,followed by binary logistic regression modeling to identify independent predictors of PPCs.Hospitalization expenditures and duration of stay were further contrasted across the study cohorts.RESULTS This study included 1941 patients who underwent liver,gallbladder,or pancreatic surgery,of whom 78 developed PPCs,resulting in an incidence rate of 4.02%.Logistic regression analysis revealed two independent predictors of PPCs in hepatobiliary surgery patients:Age≥75 year(odds ratio=8.350,95%CI:3.521-19.798,P<0.001)and prolonged anesthesia(odds ratio=1.052,95%CI:1.015-1.091,P=0.006).Patients with PPCs had significantly elevated healthcare resource utilization,including higher total hospitalization costs,increased medication expenses,longer hospital stays,and extended postoperative admissions(all P<0.001).CONCLUSION Age≥75 years and prolonged anesthesia emerged as independent predictors of PPCs following hepatobiliary surgery.These complications were correlated with protracted hospitalization and increased healthcare costs.展开更多
Image processing plays a vital role in various fields such as autonomous systems,healthcare,and cataloging,especially when integrated with deep learning(DL).It is crucial in medical diagnostics,including the early det...Image processing plays a vital role in various fields such as autonomous systems,healthcare,and cataloging,especially when integrated with deep learning(DL).It is crucial in medical diagnostics,including the early detection of diseases like chronic obstructive pulmonary disease(COPD),which claimed 3.2 million lives in 2015.COPD,a life-threatening condition often caused by prolonged exposure to lung irritants and smoking,progresses through stages.Early diagnosis through image processing can significantly improve survival rates.COPD encompasses chronic bronchitis(CB)and emphysema;CB particularly increases in smokers and generally affects individuals between 50 and 70 years old.It damages the lungs’air sacs,reducing oxygen transport and causing symptoms like coughing and shortness of breath.Treatments such as beta-agonists and inhaled steroids are used to manage symptoms and prolong lung function.Moreover,COVID-19 poses an additional risk to individuals with CB due to its impact on the respiratory system.The proposed system utilizes convolutional neural networks(CNN)to diagnose CB.In this system,CNN extracts essential and significant features from X-ray modalities,which are then fed into the neural network.The network undergoes training to recognize patterns and make accurate predictions based on the learned features.By leveraging DL techniques,the system aims to enhance the precision and reliability of CB detection.Our research specifically focuses on a subset of 189 lung disease images,carefully selected for model evaluation.To further refine the training process,various data augmentation and noise removal techniques are implemented.These techniques significantly enhance the quality of the training data,improving the model’s robustness and generalizability.As a result,the diagnostic accuracy has improved from 98.6%to 99.2%.This advancement not only validates the efficacy of our proposed model but also represents a significant improvement over existing literature.It highlights the potential of CNN-based approaches in transforming medical diagnostics through refined image analysis,learning capabilities,and automated feature extraction.展开更多
BACKGROUND Hepatitis B and C and alcoholic liver disease are the principal causes of hepaticrelated morbidity and mortality.However,evidence of the associations between diabetes without the above risk factors and hepa...BACKGROUND Hepatitis B and C and alcoholic liver disease are the principal causes of hepaticrelated morbidity and mortality.However,evidence of the associations between diabetes without the above risk factors and hepatic-related study endpoints is not well understood.In addition,the effects of associated metabolic dysfunction and exercise on hepatic outcomes are still not clear.AIM To investigate the incidence and relative hazards of cirrhosis of the liver,hepato cellular carcinoma(HCC),hepatic-related complications and mortality in patients with type 2 diabetes(T2D)who were nonalcoholic and serologically negative for hepatitis B and C in Taiwan,China.METHODS A total of 33184 T2D patients and 648746 nondiabetic subjects selected from Taiwan’s,China adult preventive health care service were linked to various National Health Insurance databases,cancer registry,and death registry to identify cirrhosis of the liver,HCC,hepatic-related complications,and mortality.The Poisson assumption and Cox proportional hazard regression model were used to estimate the incidences and relative hazards of all hepatic-related study endpoints,respectively.We also compared the risk of hepatic outcomes stratified by age,sex,associated metabolic dysfunctions,and regular exercise between T2D patients and nondiabetic subjects.RESULTS Compared with nondiabetic subjects,T2D patients had a significantly greater incidence(6.32 vs 17.20 per 10000 person-years)and greater risk of cirrhosis of the liver[adjusted hazard ratio(aHR)1.45;95%CI:1.30-1.62].The aHRs for HCC,hepatic complications,and mortality were 1.81,1.87,and 2.08,respectively.An older age,male sex,obesity,hypertension,and dyslipidemia further increased the risks of all hepatic-related study endpoints,and regular exercise decreased the risk,irrespective of diabetes status.CONCLUSION Patients with T2D are at increased risk of cirrhosis of the liver,HCC,hepatic-related complications,and mortality,and associated metabolic dysfunctions provide additional hazard.Coordinated interprofessional care for high-risk T2D patients and diabetes education,with an emphasis on the importance of physical activity,are crucial for minimizing hepatic outcomes.展开更多
BACKGROUND Metabolic dysfunction-associated steatotic liver disease(MASLD)is a leading cause of chronic liver disease globally.Current diagnostic methods,such as liver biopsies,are invasive and have limitations,highli...BACKGROUND Metabolic dysfunction-associated steatotic liver disease(MASLD)is a leading cause of chronic liver disease globally.Current diagnostic methods,such as liver biopsies,are invasive and have limitations,highlighting the need for non-invasive alternatives.AIM To investigate extracellular vesicles(EVs)as potential biomarkers for diagnosing and staging steatosis in patients with MASLD using machine learning(ML)and explainable artificial intelligence(XAI).METHODS In this single-center observational study,798 patients with metabolic dysfunction were enrolled.Of these,194 met the eligibility criteria,and 76 successfully completed all study procedures.Transient elastography was used for steatosis and fibrosis staging,and circulating plasma EV characteristics were analyzed through nanoparticle tracking.Twenty ML models were developed:Six to differentiate non-steatosis(S0)from steatosis(S1-S3);and fourteen to identify severe steatosis(S3).Models utilized EV features(size and concentration),clinical(advanced fibrosis and presence of type 2 diabetes mellitus),and anthropomorphic(sex,age,height,weight,body mass index)data.Their performance was assessed using receiver operating characteristic(ROC)-area under the curve(AUC),specificity,and sensitivity,while correlation and XAI analysis were also conducted.RESULTS The CatBoost C1a model achieved an ROC-AUC of 0.71/0.86(train/test)on average across ten random five-fold cross-validations,using EV features alone to distinguish S0 from S1-S3.The CatBoost C2h-21 model achieved an ROC-AUC of 0.81/1.00(train/test)on average across ten random three-fold cross-validations,using engineered features including EVs,clinical features like diabetes and advanced fibrosis,and anthropomorphic data like body mass index and weight for identifying severe steatosis(S3).Key predictors included EV mean size and concentration.Correlation,XAI,and SHapley Additive exPlanations analysis revealed non-linear feature relationships with steatosis stages.CONCLUSION The EV-based ML models demonstrated that the mean size and concentration of circulating plasma EVs constituted key predictors for distinguishing the absence of significant steatosis(S0)in patients with metabolic dysfunction,while the combination of EV,clinical,and anthropomorphic features improved the diagnostic accuracy for the identification of severe steatosis.The algorithmic approach using ML and XAI captured non-linear patterns between disease features and provided interpretable MASLD staging insights.However,further large multicenter studies,comparisons,and validation with histopathology and advanced imaging methods are needed.展开更多
BACKGROUND Liver cancer poses a significant public health threat.The difference between disease patterns and national policies is crucial to elucidating factors influencing hepatocellular carcinoma(HCC)incidence.AIM T...BACKGROUND Liver cancer poses a significant public health threat.The difference between disease patterns and national policies is crucial to elucidating factors influencing hepatocellular carcinoma(HCC)incidence.AIM To investigate the secular trend and disease pattern of liver cancer in Taiwan of China,Poland,and Belgium.METHODS This population-based cohort study presents the incidence,period,and cohort effects in HCC incidence between 2000 and 2019 in Taiwan of China,Poland,and Flanders,Belgium.Data on HCC were obtained from cancer registry data from Taiwan of China,Poland,and regional data from Belgium.Age-standardized incidence rates(ASIRs),annual per-centage changes,and age-period-cohort analyses were conducted by sex and period.RESULTS Taiwan of China’s ASIR decreased from 2000 to 2019(males:55.17 to 43.42,females:21.91 to 16.20,per 100000).In Poland,ASIR declined from 2000 to 2019(males:3.21 to 2.77,females:1.95 to 1.32,per 100000).However,Flanders experienced an increase in ASIR from 2000 to 2019(males:2.66 to 5.63,females:1.40 to 2.20,per 100000).In Taiwan of China,the cohort effect rate ratio increased from 1915 to 1935(males:1.02 to 1.36,females:1.04 to 1.54)and decreased from 1935 to 1989(males:1.36 to 0.22,females:1.54 to 0.20).In Poland,rate ratios consistently decreased(males:1.75 to 0.25,females:3.46 to 0.26).Flanders exhibited an increase in both males(0.14 to 2.52,1915 to 1975)and females(0.53 to 3.66,1915 to 1989).CONCLUSION Taiwan of China and Poland’s declining ASIR may be due to effective hepatitis B virus immunization and viral hepatitis therapy.Flanders’persistent increase may be tied to higher HCC risk in high hepatitis C virus risk populations.展开更多
BACKGROUND Hepatocellular carcinoma(HCC)is one of the most common malignancies worldwide.However,the number of patients with chronic kidney disease(CKD)is on the rise because of the increase in lifestyle-related disea...BACKGROUND Hepatocellular carcinoma(HCC)is one of the most common malignancies worldwide.However,the number of patients with chronic kidney disease(CKD)is on the rise because of the increase in lifestyle-related diseases.AIM To establish a tailored management strategy for HCC patients,we evaluated the impact of comorbid renal dysfunction(RD),as stratified by using the estimated glomerular filtration rate(EGFR),and assessed the oncologic validity of hepatectomy for HCC patients with RD.METHODS We enrolled 800 HCC patients who underwent hepatectomy between 1997 and 2015 at our university hospital.We categorized patients into two(RD,EGFR<60 mL/min/1.73 m^(2);non-RD,EGFR≥60 mL/min/1.73 m^(2))and three groups(severe CKD,EGFR<30 mL/min/1.73 m^(2);mild CKD,30≤EGFR<60 mL/min/1.73 m2;control,EGFR≥60 mL/min/1.73 m^(2))according to renal function as defined by the EGFR.Overall survival(OS)and recurrence-free survival(RFS)were compared among these groups with the log-rank test,and we also analyzed survival by using a propensity score matching(PSM)model to exclude the influence of patient characteristics.The mean postoperative observation period was 64.7±53.0 mo.RESULTS The RD patients were significantly older and had lower serum total bilirubin,aspartate aminotransferase,and aspartate aminotransferase levels than the non-RD patients(P<0.0001,P<0.001,P<0.05,and P<0.01,respectively).No patient received maintenance hemodialysis after surgery.Although the overall postoperative complication rates were similar between the RD and non-RD patients,the proportions of postoperative bleeding and surgical site infection were significantly higher in the RD patients(5.5%vs 1.8%;P<0.05,3.9%vs 1.8%;P<0.05,respectively),and postoperative bleeding was the highest in the severe CKD group(P<0.05).Regardless of the degree of comorbid RD,OS and RFS were comparable,even after PSM between the RD and non-RD groups to exclude the influence of patient characteristics,liver function,and other causes of death.CONCLUSION Comorbid mild RD had a negligible impact on the prognosis of HCC patients who underwent curative hepatectomy with appropriate perioperative management,and close attention to severe CKD is necessary to prevent postoperative bleeding and surgical site infection.展开更多
Emergence refers to the existence or formation of collective behaviors in complex systems.Here,we develop a theoretical framework based on the eigen microstate theory to analyze the emerging phenomena and dynamic evol...Emergence refers to the existence or formation of collective behaviors in complex systems.Here,we develop a theoretical framework based on the eigen microstate theory to analyze the emerging phenomena and dynamic evolution of complex system.In this framework,the statistical ensemble composed of M microstates of a complex system with N agents is defined by the normalized N×M matrix A,whose columns represent microstates and order of row is consist with the time.The ensemble matrix A can be decomposed as■,where r=min(N,M),eigenvalueσIbehaves as the probability amplitude of the eigen microstate U_I so that■and U_I evolves following V_I.In a disorder complex system,there is no dominant eigenvalue and eigen microstate.When a probability amplitudeσIbecomes finite in the thermodynamic limit,there is a condensation of the eigen microstate UIin analogy to the Bose–Einstein condensation of Bose gases.This indicates the emergence of U_I and a phase transition in complex system.Our framework has been applied successfully to equilibrium threedimensional Ising model,climate system and stock markets.We anticipate that our eigen microstate method can be used to study non-equilibrium complex systems with unknown orderparameters,such as phase transitions of collective motion and tipping points in climate systems and ecosystems.展开更多
Objective To evaluate the association between serum uric acid(SUA)and kidney function decline.Methods Data was obtained from the China Health and Retirement Longitudinal Study on the Chinese middle-aged and older popu...Objective To evaluate the association between serum uric acid(SUA)and kidney function decline.Methods Data was obtained from the China Health and Retirement Longitudinal Study on the Chinese middle-aged and older population for analysis.The kidney function decline was defined as an annual estimated glomerular filtration rate(e GFR)decrease by>3 mL/min per 1.73 m^(2).Multivariable logistic regression was applied to determine the association between SUA and kidney function decline.The shape of the association was investigated by restricted cubic splines.Results A total of 7,346 participants were included,of which 1,004 individuals(13.67%)developed kidney function decline during the follow-up of 4 years.A significant dose-response relation was recorded between SUA and the kidney function decline(OR 1.14,95%CI 1.03-1.27),as the risk of kidney function decline increased by 14%per 1 mg/d L increase in SUA.In the subgroup analyses,such a relation was only recorded among women(OR 1.22,95%CI 1.03-1.45),those aged<60 years(OR 1.22,95%CI 1.05-1.42),and those without hypertension and without diabetes(OR 1.22,95%CI 1.06-1.41).Although the dose-response relation was not observed in men,the high level of SUA was related to kidney function decline(OR 1.83,95%CI 1.05-3.17).The restricted cubic spline analysis indicated that SUA>5 mg/dL was associated with a significantly higher risk of kidney function decline.Conclusion The SUA level was associated with kidney function decline.An elevation of SUA should therefore be addressed to prevent possible kidney impairment and dysfunction.展开更多
Electronic absorption bands of conjugated linear carbon chain molecules, namely polyynes H(C≡C)nH (n=5-7), are exploited to devise light-polarizing films applicable to the UV. Laser ablated polyynes are separated in ...Electronic absorption bands of conjugated linear carbon chain molecules, namely polyynes H(C≡C)nH (n=5-7), are exploited to devise light-polarizing films applicable to the UV. Laser ablated polyynes are separated in size and dispersed in a film of polyvinyl alcohol (PVA), which is stretched to align the trapped linear polyyne molecules inside. As a nature of the structural anisotropy, transition dipole of the UV absorption for polyyne molecules is in parallel with the molecular axis and the absorption occurs only for the electromagnetic wave having the amplitude of its electric vector along the molecular axis. Aligned and fixed orientationally in the solid PVA film, polyyne molecules act as selective absorbers of one of the polarization components of incident light at particular wavelength. Using a light source of linearly polarized UV light, whose direction of polarization is rotatable, angular dependence of the absorption intensity is investigated for the stretched PVA film containing aligned polyyne molecules and analyzed in terms of an order parameter in the theory of linear dichroism.展开更多
BACKGROUND Intrahepatic cholangiocarcinoma(ICC)is one of the most aggressive malignancies.However,because of its scarcity there are limited population-based data available for investigations into its epidemiologic cha...BACKGROUND Intrahepatic cholangiocarcinoma(ICC)is one of the most aggressive malignancies.However,because of its scarcity there are limited population-based data available for investigations into its epidemiologic characteristics.In Taiwan,we have a national cancer registry database that can be used to evaluate the secular trends of ICC.AIM To evaluate secular trends of ICC according to age,sex,and risk factors in Taiwan.METHODS In this population-based study,we used the national Taiwan Cancer Registry database.Age-standardized and relative percent changes in incidence rates were used to describe secular trends in incidence rates and sex ratios of ICC in Taiwan.RESULTS The age-standardized ICC incidence rate among males increased from 1.51 per 100000 in 1993-1997 to 4.07 per 100000 in 2013-2017 and among female from 1.73 per 100000 to 2.95 per 100000.The incidence in females tended to plateau after 2008-2012.For males,the ICC incidence increased as age increased.In the long-term incidence trend of ICC in females,the incidence of the four age groups(40-44,45-49,50-54 and 55-59 years)remained stable in different years;although,the incidence of the 60-64 group had a peak in 2003-2007,and the peak incidence of the 65-69 and 70-74 groups occurred in 2008-2012.Among males,beginning at the age of 65,there were increases in the incidence of ICC for the period of 2003-2017 as compared with females in the period of 2003-2017.CONCLUSION Increased incidence of ICC occurred in Taiwan over the past two decades.The increased incidence has progressively shifted toward younger people for both males and females.展开更多
Currently,digital certificate systems based on blockchain have been extensively developed and adopted.However,most of them do not take into account the certificate quality.To evaluate the credibility of certificates i...Currently,digital certificate systems based on blockchain have been extensively developed and adopted.However,most of them do not take into account the certificate quality.To evaluate the credibility of certificates issued by educational institutions,we propose a novel blockchain-based system with credit self-adjustment(BC-CS).In BC-CS,employers can provide feedback according to the performances of their employees(i.e.,students)holding different certificates.Based on the feedback,BC-CS automatically adjusts the certificate credits by using our proposed credit self-adjustment algorithm.To verify the feasibility of our proposed system,a decentralized application prototype has been developed on an Ethereum network.Experimental results demonstrate that the proposed system can fully support multistep accreditation and automatic adjustment for certificate credit.展开更多
In recent years,mobile edge computing has attracted a considerable amount of attention from both academia and industry through its many advantages(such as low latency,computation efficiency and privacy)caused by its l...In recent years,mobile edge computing has attracted a considerable amount of attention from both academia and industry through its many advantages(such as low latency,computation efficiency and privacy)caused by its local model of providing storage and computation resources.展开更多
In trying to explain why Hong Kong of China ranks highest in life expectancy in the world,we review what various experts are hypothesizing,and how data science methods may be used to provide more evidence-based conclu...In trying to explain why Hong Kong of China ranks highest in life expectancy in the world,we review what various experts are hypothesizing,and how data science methods may be used to provide more evidence-based conclusions.While more data become available,we find some data analysis studies were too simplistic,while others too overwhelming in answering this challenging question.We find the approach that analyzes life expectancy related data(mortality causes and rate for different cohorts)inspiring,and use this approach to study a carefully selected set of targets for comparison.In discussing the factors that matter,we argue that it is more reasonable to try to identify a set of factors that together explain the phenomenon.展开更多
The purpose of this study is to present the numerical performancesand interpretations of the SEIR nonlinear system based on the Zika virusspreading by using the stochastic neural networks based intelligent computingso...The purpose of this study is to present the numerical performancesand interpretations of the SEIR nonlinear system based on the Zika virusspreading by using the stochastic neural networks based intelligent computingsolver. The epidemic form of the nonlinear system represents the four dynamicsof the patients, susceptible patients S(y), exposed patients hospitalized inhospital E(y), infected patients I(y), and recovered patients R(y), i.e., SEIRmodel. The computing numerical outcomes and performances of the systemare examined by using the artificial neural networks (ANNs) and the scaledconjugate gradient (SCG) for the training of the networks, i.e., ANNs-SCG.The correctness of the ANNs-SCG scheme is observed by comparing theproposed and reference solutions for three cases of the SEIR model to solvethe nonlinear system based on the Zika virus spreading dynamics throughthe knacks of ANNs-SCG procedure based on exhaustive experimentations.The outcomes of the ANNs-SCG algorithm are found consistently in goodagreement with standard numerical solutions with negligible errors. Moreover,the procedure’s constancy, dependability, and exactness are perceived by usingthe values of state transitions, error histogram measures, correlation, andregression analysis.展开更多
Recent decades have witnessed several infectious disease outbreaks,including the coronavirus disease(COVID-19)pandemic,which had catastrophic impacts on societies around the globe.At the same time,the twenty-first cen...Recent decades have witnessed several infectious disease outbreaks,including the coronavirus disease(COVID-19)pandemic,which had catastrophic impacts on societies around the globe.At the same time,the twenty-first century has experienced an unprecedented era of technological development and demographic changes:exploding population growth,increased airline flights,and increased rural-to-urban migration,with an estimated 281 million international migrants worldwide in 2020,despite COVID-19 movement restrictions.In this review,we synthesized 195 research articles that examined the association between human movement and infectious disease outbreaks to understand the extent to which human mobility has increased the risk of infectious disease outbreaks.This article covers eight infectious diseases,ranging from respiratory illnesses to sexually transmitted and vector-borne diseases.The review revealed a strong association between human mobility and infectious disease spread,particularly strong for respiratory illnesses like COVID-19 and Influenza.Despite significant research into the relationship between infectious diseases and human mobility,four knowledge gaps were identified based on reviewed literature in this study:1)although some studies have used big data in investigating infectious diseases,the efforts are limited(with the exception of COVID-19 disease),2)while some research has explored the use of multiple data sources,there has been limited focus on fully integrating these data into comprehensive analyses,3)limited research on the global impact of mobility on the spread of infectious disease with most studies focusing on local or regional outbreaks,and 4)lack of standardization in the methodology for measuring the impacts of human mobility on infectious disease spread.By tackling the recognized knowledge gaps and adopting holistic,interdisciplinary methods,forthcoming research has the potential to substantially enhance our comprehension of the intricate interplay between human mobility and infectious diseases.展开更多
Geospatial social media(GSM)data has been increasingly used in public health due to its rich,timely,and accessible spatial information,particularly in infectious disease research.This review synthesized 86 research ar...Geospatial social media(GSM)data has been increasingly used in public health due to its rich,timely,and accessible spatial information,particularly in infectious disease research.This review synthesized 86 research articles that use GSM data in infectious diseases published between December 2013 and March 2022.These articles cover 12 infectious disease types ranging from respiratory infectious diseases to sexually transmitted diseases with spatial levels varying from the neighborhood,county,state,and country.We categorized these studies into three major infectious disease research domains:surveillance,explanation,and prediction.With the assistance of advanced computing,statistical and spatial methods,GSM data has been widely and deeply applied to these domains,particularly in surveillance and explanation domains.We further identified four knowledge gaps in terms of contextual information use,application scopes,spatiotemporal dimension,and data limitations and proposed innovation opportunities for future research.Ourfindings will contribute to a better understanding of using GSM data in infectious diseases studies and provide insights into strategies for using GSM data more effectively in future research.展开更多
文摘The widespread usage of rechargeable batteries in portable devices,electric vehicles,and energy storage systems has underscored the importance for accurately predicting their lifetimes.However,data scarcity often limits the accuracy of prediction models,which is escalated by the incompletion of data induced by the issues such as sensor failures.To address these challenges,we propose a novel approach to accommodate data insufficiency through achieving external information from incomplete data samples,which are usually discarded in existing studies.In order to fully unleash the prediction power of incomplete data,we have investigated the Multiple Imputation by Chained Equations(MICE)method that diversifies the training data through exploring the potential data patterns.The experimental results demonstrate that the proposed method significantly outperforms the baselines in the most considered scenarios while reducing the prediction root mean square error(RMSE)by up to 18.9%.Furthermore,we have also observed that the penetration of incomplete data benefits the explainability of the prediction model through facilitating the feature selection.
基金National Natural Science Foundation of China(11971211,12171388).
文摘Complex network models are frequently employed for simulating and studyingdiverse real-world complex systems.Among these models,scale-free networks typically exhibit greater fragility to malicious attacks.Consequently,enhancing the robustness of scale-free networks has become a pressing issue.To address this problem,this paper proposes a Multi-Granularity Integration Algorithm(MGIA),which aims to improve the robustness of scale-free networks while keeping the initial degree of each node unchanged,ensuring network connectivity and avoiding the generation of multiple edges.The algorithm generates a multi-granularity structure from the initial network to be optimized,then uses different optimization strategies to optimize the networks at various granular layers in this structure,and finally realizes the information exchange between different granular layers,thereby further enhancing the optimization effect.We propose new network refresh,crossover,and mutation operators to ensure that the optimized network satisfies the given constraints.Meanwhile,we propose new network similarity and network dissimilarity evaluation metrics to improve the effectiveness of the optimization operators in the algorithm.In the experiments,the MGIA enhances the robustness of the scale-free network by 67.6%.This improvement is approximately 17.2%higher than the optimization effects achieved by eight currently existing complex network robustness optimization algorithms.
基金funded by the Deanship of Graduate Studies and Scientific Research at Jouf University under Grant Number(DGSSR-2023-02-02516).
文摘This study presents a comprehensive and secure architectural framework for the Internet of Medical Things(IoMT),integrating the foundational principles of the Confidentiality,Integrity,and Availability(CIA)triad along with authentication mechanisms.Leveraging advanced Machine Learning(ML)and Deep Learning(DL)techniques,the proposed system is designed to safeguard Patient-Generated Health Data(PGHD)across interconnected medical devices.Given the increasing complexity and scale of cyber threats in IoMT environments,the integration of Intrusion Detection and Prevention Systems(IDPS)with intelligent analytics is critical.Our methodology employs both standalone and hybrid ML&DL models to automate threat detection and enable real-time analysis,while ensuring rapid and accurate responses to a diverse array of attacks.Emphasis is placed on systematic model evaluation using detection metrics such as accuracy,False Alarm Rate(FAR),and False Discovery Rate(FDR),with performance validation through cross-validation and statistical significance testing.Experimental results based on the Edge-IIoTset dataset demonstrate the superior performance of ensemble-based ML models such as Extreme Gradient Boosting(XGB)and hybrid DL models such as Convolutional Neural Networks with Autoencoders(CNN+AE),which achieved detection accuracies of 96%and 98%,respectively,with notably low FARs.These findings underscore the effectiveness of combining traditional security principles with advanced AI-driven methodologies to ensure secure,resilient,and trustworthy healthcare systems within the IoMT ecosystem.
基金Project supported by Yunnan Fundamental Research Projects(Grant No.202401AT070359)。
文摘Identifying vital nodes is one of the core issues of network science,and is crucial for epidemic prevention and control,network security maintenance,and biomedical research and development.In this paper,a new vital nodes identification method,named degree and cycle ratio(DC),is proposed by integrating degree centrality(weightα)and cycle ratio(weight 1-α).The results show that the dynamic observations and weightαare nonlinear and non-monotonicity(i.e.,there exists an optimal valueα^(*)forα),and that DC performs better than a single index in most networks.According to the value ofα^(*),networks are classified into degree-dominant networks(α^(*)>0.5)and cycle-dominant networks(α^(*)<0.5).Specifically,in most degree-dominant networks(such as Chengdu-BUS,Chongqing-BUS and Beijing-BUS),degree is dominant in the identification of vital nodes,but the identification effect can be improved by adding cycle structure information to the nodes.In most cycle-dominant networks(such as Email,Wiki and Hamsterster),the cycle ratio is dominant in the identification of vital nodes,but the effect can be notably enhanced by additional node degree information.Finally,interestingly,in Lancichinetti-Fortunato-Radicchi(LFR)synthesis networks,the cycle-dominant network is observed.
基金Supported by the Beijing Tsinghua Changgung Hospital Fund,China,No.12023C01005.
文摘BACKGROUND Postoperative pulmonary complications(PPCs)are the most common complications following major upper abdominal surgeries,particularly hepatobiliary procedures,and significantly compromise surgical outcomes and patients’quality of life.Although the adoption of laparoscopy has lowered their incidence,PPCs remain a frequent and serious concern after hepatobiliary surgery.Existing research on risk factors specific to hepatobiliary surgeries is limited,particularly regarding the epidemiology and risk factors of PPCs in liver and gallbladder surgeries in China.Therefore,this study aimed to investigate the risk factors for PPCs in a large hepatobiliary center.AIM To identify the incidence and risk factors for PPCs following hepatobiliary surgery based on perioperative variables.METHODS Retrospective data were collected from patients who underwent liver,gallbladder,or pancreatic surgery at a hepatobiliary center in China between May 2023 and December 2023.We retrospectively reviewed comprehensive medical records to extract demographic and hospital admission information for determining PPC incidence.Statistically significant variables were initially screened through univariate analysis,followed by binary logistic regression modeling to identify independent predictors of PPCs.Hospitalization expenditures and duration of stay were further contrasted across the study cohorts.RESULTS This study included 1941 patients who underwent liver,gallbladder,or pancreatic surgery,of whom 78 developed PPCs,resulting in an incidence rate of 4.02%.Logistic regression analysis revealed two independent predictors of PPCs in hepatobiliary surgery patients:Age≥75 year(odds ratio=8.350,95%CI:3.521-19.798,P<0.001)and prolonged anesthesia(odds ratio=1.052,95%CI:1.015-1.091,P=0.006).Patients with PPCs had significantly elevated healthcare resource utilization,including higher total hospitalization costs,increased medication expenses,longer hospital stays,and extended postoperative admissions(all P<0.001).CONCLUSION Age≥75 years and prolonged anesthesia emerged as independent predictors of PPCs following hepatobiliary surgery.These complications were correlated with protracted hospitalization and increased healthcare costs.
文摘Image processing plays a vital role in various fields such as autonomous systems,healthcare,and cataloging,especially when integrated with deep learning(DL).It is crucial in medical diagnostics,including the early detection of diseases like chronic obstructive pulmonary disease(COPD),which claimed 3.2 million lives in 2015.COPD,a life-threatening condition often caused by prolonged exposure to lung irritants and smoking,progresses through stages.Early diagnosis through image processing can significantly improve survival rates.COPD encompasses chronic bronchitis(CB)and emphysema;CB particularly increases in smokers and generally affects individuals between 50 and 70 years old.It damages the lungs’air sacs,reducing oxygen transport and causing symptoms like coughing and shortness of breath.Treatments such as beta-agonists and inhaled steroids are used to manage symptoms and prolong lung function.Moreover,COVID-19 poses an additional risk to individuals with CB due to its impact on the respiratory system.The proposed system utilizes convolutional neural networks(CNN)to diagnose CB.In this system,CNN extracts essential and significant features from X-ray modalities,which are then fed into the neural network.The network undergoes training to recognize patterns and make accurate predictions based on the learned features.By leveraging DL techniques,the system aims to enhance the precision and reliability of CB detection.Our research specifically focuses on a subset of 189 lung disease images,carefully selected for model evaluation.To further refine the training process,various data augmentation and noise removal techniques are implemented.These techniques significantly enhance the quality of the training data,improving the model’s robustness and generalizability.As a result,the diagnostic accuracy has improved from 98.6%to 99.2%.This advancement not only validates the efficacy of our proposed model but also represents a significant improvement over existing literature.It highlights the potential of CNN-based approaches in transforming medical diagnostics through refined image analysis,learning capabilities,and automated feature extraction.
基金Supported by The Far Eastern Memorial Hospital,No.FEMH-2022-C-015,No.FEMH-2022-C-017 and No.FEMH-2023-C-082.
文摘BACKGROUND Hepatitis B and C and alcoholic liver disease are the principal causes of hepaticrelated morbidity and mortality.However,evidence of the associations between diabetes without the above risk factors and hepatic-related study endpoints is not well understood.In addition,the effects of associated metabolic dysfunction and exercise on hepatic outcomes are still not clear.AIM To investigate the incidence and relative hazards of cirrhosis of the liver,hepato cellular carcinoma(HCC),hepatic-related complications and mortality in patients with type 2 diabetes(T2D)who were nonalcoholic and serologically negative for hepatitis B and C in Taiwan,China.METHODS A total of 33184 T2D patients and 648746 nondiabetic subjects selected from Taiwan’s,China adult preventive health care service were linked to various National Health Insurance databases,cancer registry,and death registry to identify cirrhosis of the liver,HCC,hepatic-related complications,and mortality.The Poisson assumption and Cox proportional hazard regression model were used to estimate the incidences and relative hazards of all hepatic-related study endpoints,respectively.We also compared the risk of hepatic outcomes stratified by age,sex,associated metabolic dysfunctions,and regular exercise between T2D patients and nondiabetic subjects.RESULTS Compared with nondiabetic subjects,T2D patients had a significantly greater incidence(6.32 vs 17.20 per 10000 person-years)and greater risk of cirrhosis of the liver[adjusted hazard ratio(aHR)1.45;95%CI:1.30-1.62].The aHRs for HCC,hepatic complications,and mortality were 1.81,1.87,and 2.08,respectively.An older age,male sex,obesity,hypertension,and dyslipidemia further increased the risks of all hepatic-related study endpoints,and regular exercise decreased the risk,irrespective of diabetes status.CONCLUSION Patients with T2D are at increased risk of cirrhosis of the liver,HCC,hepatic-related complications,and mortality,and associated metabolic dysfunctions provide additional hazard.Coordinated interprofessional care for high-risk T2D patients and diabetes education,with an emphasis on the importance of physical activity,are crucial for minimizing hepatic outcomes.
文摘BACKGROUND Metabolic dysfunction-associated steatotic liver disease(MASLD)is a leading cause of chronic liver disease globally.Current diagnostic methods,such as liver biopsies,are invasive and have limitations,highlighting the need for non-invasive alternatives.AIM To investigate extracellular vesicles(EVs)as potential biomarkers for diagnosing and staging steatosis in patients with MASLD using machine learning(ML)and explainable artificial intelligence(XAI).METHODS In this single-center observational study,798 patients with metabolic dysfunction were enrolled.Of these,194 met the eligibility criteria,and 76 successfully completed all study procedures.Transient elastography was used for steatosis and fibrosis staging,and circulating plasma EV characteristics were analyzed through nanoparticle tracking.Twenty ML models were developed:Six to differentiate non-steatosis(S0)from steatosis(S1-S3);and fourteen to identify severe steatosis(S3).Models utilized EV features(size and concentration),clinical(advanced fibrosis and presence of type 2 diabetes mellitus),and anthropomorphic(sex,age,height,weight,body mass index)data.Their performance was assessed using receiver operating characteristic(ROC)-area under the curve(AUC),specificity,and sensitivity,while correlation and XAI analysis were also conducted.RESULTS The CatBoost C1a model achieved an ROC-AUC of 0.71/0.86(train/test)on average across ten random five-fold cross-validations,using EV features alone to distinguish S0 from S1-S3.The CatBoost C2h-21 model achieved an ROC-AUC of 0.81/1.00(train/test)on average across ten random three-fold cross-validations,using engineered features including EVs,clinical features like diabetes and advanced fibrosis,and anthropomorphic data like body mass index and weight for identifying severe steatosis(S3).Key predictors included EV mean size and concentration.Correlation,XAI,and SHapley Additive exPlanations analysis revealed non-linear feature relationships with steatosis stages.CONCLUSION The EV-based ML models demonstrated that the mean size and concentration of circulating plasma EVs constituted key predictors for distinguishing the absence of significant steatosis(S0)in patients with metabolic dysfunction,while the combination of EV,clinical,and anthropomorphic features improved the diagnostic accuracy for the identification of severe steatosis.The algorithmic approach using ML and XAI captured non-linear patterns between disease features and provided interpretable MASLD staging insights.However,further large multicenter studies,comparisons,and validation with histopathology and advanced imaging methods are needed.
文摘BACKGROUND Liver cancer poses a significant public health threat.The difference between disease patterns and national policies is crucial to elucidating factors influencing hepatocellular carcinoma(HCC)incidence.AIM To investigate the secular trend and disease pattern of liver cancer in Taiwan of China,Poland,and Belgium.METHODS This population-based cohort study presents the incidence,period,and cohort effects in HCC incidence between 2000 and 2019 in Taiwan of China,Poland,and Flanders,Belgium.Data on HCC were obtained from cancer registry data from Taiwan of China,Poland,and regional data from Belgium.Age-standardized incidence rates(ASIRs),annual per-centage changes,and age-period-cohort analyses were conducted by sex and period.RESULTS Taiwan of China’s ASIR decreased from 2000 to 2019(males:55.17 to 43.42,females:21.91 to 16.20,per 100000).In Poland,ASIR declined from 2000 to 2019(males:3.21 to 2.77,females:1.95 to 1.32,per 100000).However,Flanders experienced an increase in ASIR from 2000 to 2019(males:2.66 to 5.63,females:1.40 to 2.20,per 100000).In Taiwan of China,the cohort effect rate ratio increased from 1915 to 1935(males:1.02 to 1.36,females:1.04 to 1.54)and decreased from 1935 to 1989(males:1.36 to 0.22,females:1.54 to 0.20).In Poland,rate ratios consistently decreased(males:1.75 to 0.25,females:3.46 to 0.26).Flanders exhibited an increase in both males(0.14 to 2.52,1915 to 1975)and females(0.53 to 3.66,1915 to 1989).CONCLUSION Taiwan of China and Poland’s declining ASIR may be due to effective hepatitis B virus immunization and viral hepatitis therapy.Flanders’persistent increase may be tied to higher HCC risk in high hepatitis C virus risk populations.
文摘BACKGROUND Hepatocellular carcinoma(HCC)is one of the most common malignancies worldwide.However,the number of patients with chronic kidney disease(CKD)is on the rise because of the increase in lifestyle-related diseases.AIM To establish a tailored management strategy for HCC patients,we evaluated the impact of comorbid renal dysfunction(RD),as stratified by using the estimated glomerular filtration rate(EGFR),and assessed the oncologic validity of hepatectomy for HCC patients with RD.METHODS We enrolled 800 HCC patients who underwent hepatectomy between 1997 and 2015 at our university hospital.We categorized patients into two(RD,EGFR<60 mL/min/1.73 m^(2);non-RD,EGFR≥60 mL/min/1.73 m^(2))and three groups(severe CKD,EGFR<30 mL/min/1.73 m^(2);mild CKD,30≤EGFR<60 mL/min/1.73 m2;control,EGFR≥60 mL/min/1.73 m^(2))according to renal function as defined by the EGFR.Overall survival(OS)and recurrence-free survival(RFS)were compared among these groups with the log-rank test,and we also analyzed survival by using a propensity score matching(PSM)model to exclude the influence of patient characteristics.The mean postoperative observation period was 64.7±53.0 mo.RESULTS The RD patients were significantly older and had lower serum total bilirubin,aspartate aminotransferase,and aspartate aminotransferase levels than the non-RD patients(P<0.0001,P<0.001,P<0.05,and P<0.01,respectively).No patient received maintenance hemodialysis after surgery.Although the overall postoperative complication rates were similar between the RD and non-RD patients,the proportions of postoperative bleeding and surgical site infection were significantly higher in the RD patients(5.5%vs 1.8%;P<0.05,3.9%vs 1.8%;P<0.05,respectively),and postoperative bleeding was the highest in the severe CKD group(P<0.05).Regardless of the degree of comorbid RD,OS and RFS were comparable,even after PSM between the RD and non-RD groups to exclude the influence of patient characteristics,liver function,and other causes of death.CONCLUSION Comorbid mild RD had a negligible impact on the prognosis of HCC patients who underwent curative hepatectomy with appropriate perioperative management,and close attention to severe CKD is necessary to prevent postoperative bleeding and surgical site infection.
基金supported by the Key Research Program of Frontier Sciences,Chinese Academy of Sciences(Grant No.QYZD-SSW-SYS019)。
文摘Emergence refers to the existence or formation of collective behaviors in complex systems.Here,we develop a theoretical framework based on the eigen microstate theory to analyze the emerging phenomena and dynamic evolution of complex system.In this framework,the statistical ensemble composed of M microstates of a complex system with N agents is defined by the normalized N×M matrix A,whose columns represent microstates and order of row is consist with the time.The ensemble matrix A can be decomposed as■,where r=min(N,M),eigenvalueσIbehaves as the probability amplitude of the eigen microstate U_I so that■and U_I evolves following V_I.In a disorder complex system,there is no dominant eigenvalue and eigen microstate.When a probability amplitudeσIbecomes finite in the thermodynamic limit,there is a condensation of the eigen microstate UIin analogy to the Bose–Einstein condensation of Bose gases.This indicates the emergence of U_I and a phase transition in complex system.Our framework has been applied successfully to equilibrium threedimensional Ising model,climate system and stock markets.We anticipate that our eigen microstate method can be used to study non-equilibrium complex systems with unknown orderparameters,such as phase transitions of collective motion and tipping points in climate systems and ecosystems.
文摘Objective To evaluate the association between serum uric acid(SUA)and kidney function decline.Methods Data was obtained from the China Health and Retirement Longitudinal Study on the Chinese middle-aged and older population for analysis.The kidney function decline was defined as an annual estimated glomerular filtration rate(e GFR)decrease by>3 mL/min per 1.73 m^(2).Multivariable logistic regression was applied to determine the association between SUA and kidney function decline.The shape of the association was investigated by restricted cubic splines.Results A total of 7,346 participants were included,of which 1,004 individuals(13.67%)developed kidney function decline during the follow-up of 4 years.A significant dose-response relation was recorded between SUA and the kidney function decline(OR 1.14,95%CI 1.03-1.27),as the risk of kidney function decline increased by 14%per 1 mg/d L increase in SUA.In the subgroup analyses,such a relation was only recorded among women(OR 1.22,95%CI 1.03-1.45),those aged<60 years(OR 1.22,95%CI 1.05-1.42),and those without hypertension and without diabetes(OR 1.22,95%CI 1.06-1.41).Although the dose-response relation was not observed in men,the high level of SUA was related to kidney function decline(OR 1.83,95%CI 1.05-3.17).The restricted cubic spline analysis indicated that SUA>5 mg/dL was associated with a significantly higher risk of kidney function decline.Conclusion The SUA level was associated with kidney function decline.An elevation of SUA should therefore be addressed to prevent possible kidney impairment and dysfunction.
基金supported by the MEXT-Supported Program for the Strategic Research Foundation at Private Universities entitled Establishing a Best-Energy-Mix Research Center to Promote the Use of Solar Energy subsidized from the Ministry of Education, Culture, Sports, Science and Technology of Japan and Kindai University
文摘Electronic absorption bands of conjugated linear carbon chain molecules, namely polyynes H(C≡C)nH (n=5-7), are exploited to devise light-polarizing films applicable to the UV. Laser ablated polyynes are separated in size and dispersed in a film of polyvinyl alcohol (PVA), which is stretched to align the trapped linear polyyne molecules inside. As a nature of the structural anisotropy, transition dipole of the UV absorption for polyyne molecules is in parallel with the molecular axis and the absorption occurs only for the electromagnetic wave having the amplitude of its electric vector along the molecular axis. Aligned and fixed orientationally in the solid PVA film, polyyne molecules act as selective absorbers of one of the polarization components of incident light at particular wavelength. Using a light source of linearly polarized UV light, whose direction of polarization is rotatable, angular dependence of the absorption intensity is investigated for the stretched PVA film containing aligned polyyne molecules and analyzed in terms of an order parameter in the theory of linear dichroism.
文摘BACKGROUND Intrahepatic cholangiocarcinoma(ICC)is one of the most aggressive malignancies.However,because of its scarcity there are limited population-based data available for investigations into its epidemiologic characteristics.In Taiwan,we have a national cancer registry database that can be used to evaluate the secular trends of ICC.AIM To evaluate secular trends of ICC according to age,sex,and risk factors in Taiwan.METHODS In this population-based study,we used the national Taiwan Cancer Registry database.Age-standardized and relative percent changes in incidence rates were used to describe secular trends in incidence rates and sex ratios of ICC in Taiwan.RESULTS The age-standardized ICC incidence rate among males increased from 1.51 per 100000 in 1993-1997 to 4.07 per 100000 in 2013-2017 and among female from 1.73 per 100000 to 2.95 per 100000.The incidence in females tended to plateau after 2008-2012.For males,the ICC incidence increased as age increased.In the long-term incidence trend of ICC in females,the incidence of the four age groups(40-44,45-49,50-54 and 55-59 years)remained stable in different years;although,the incidence of the 60-64 group had a peak in 2003-2007,and the peak incidence of the 65-69 and 70-74 groups occurred in 2008-2012.Among males,beginning at the age of 65,there were increases in the incidence of ICC for the period of 2003-2017 as compared with females in the period of 2003-2017.CONCLUSION Increased incidence of ICC occurred in Taiwan over the past two decades.The increased incidence has progressively shifted toward younger people for both males and females.
文摘Currently,digital certificate systems based on blockchain have been extensively developed and adopted.However,most of them do not take into account the certificate quality.To evaluate the credibility of certificates issued by educational institutions,we propose a novel blockchain-based system with credit self-adjustment(BC-CS).In BC-CS,employers can provide feedback according to the performances of their employees(i.e.,students)holding different certificates.Based on the feedback,BC-CS automatically adjusts the certificate credits by using our proposed credit self-adjustment algorithm.To verify the feasibility of our proposed system,a decentralized application prototype has been developed on an Ethereum network.Experimental results demonstrate that the proposed system can fully support multistep accreditation and automatic adjustment for certificate credit.
文摘In recent years,mobile edge computing has attracted a considerable amount of attention from both academia and industry through its many advantages(such as low latency,computation efficiency and privacy)caused by its local model of providing storage and computation resources.
基金support of funding(No.UGC/IDS(R)11/21)from the Hong Kong SAR Government.
文摘In trying to explain why Hong Kong of China ranks highest in life expectancy in the world,we review what various experts are hypothesizing,and how data science methods may be used to provide more evidence-based conclusions.While more data become available,we find some data analysis studies were too simplistic,while others too overwhelming in answering this challenging question.We find the approach that analyzes life expectancy related data(mortality causes and rate for different cohorts)inspiring,and use this approach to study a carefully selected set of targets for comparison.In discussing the factors that matter,we argue that it is more reasonable to try to identify a set of factors that together explain the phenomenon.
基金support from the NSRF via the program anagement Unit for Human Resources&Institutional Development,Research and Innovation[Grant number B05F640183]Chiang Mai University.Watcharaporn Cholamjiak would like to thank National Research Council of Thailand (N42A650334)Thailand Science Research and Innovation,the University of Phayao (Grant No.FF66-UoE).
文摘The purpose of this study is to present the numerical performancesand interpretations of the SEIR nonlinear system based on the Zika virusspreading by using the stochastic neural networks based intelligent computingsolver. The epidemic form of the nonlinear system represents the four dynamicsof the patients, susceptible patients S(y), exposed patients hospitalized inhospital E(y), infected patients I(y), and recovered patients R(y), i.e., SEIRmodel. The computing numerical outcomes and performances of the systemare examined by using the artificial neural networks (ANNs) and the scaledconjugate gradient (SCG) for the training of the networks, i.e., ANNs-SCG.The correctness of the ANNs-SCG scheme is observed by comparing theproposed and reference solutions for three cases of the SEIR model to solvethe nonlinear system based on the Zika virus spreading dynamics throughthe knacks of ANNs-SCG procedure based on exhaustive experimentations.The outcomes of the ANNs-SCG algorithm are found consistently in goodagreement with standard numerical solutions with negligible errors. Moreover,the procedure’s constancy, dependability, and exactness are perceived by usingthe values of state transitions, error histogram measures, correlation, andregression analysis.
基金supported by the National Institute of Allergy and Infectious Diseases of the National Institutes of Health under Award Number[3R01AI127203-04S1]National Science Foundation under Award Number[2028791].
文摘Recent decades have witnessed several infectious disease outbreaks,including the coronavirus disease(COVID-19)pandemic,which had catastrophic impacts on societies around the globe.At the same time,the twenty-first century has experienced an unprecedented era of technological development and demographic changes:exploding population growth,increased airline flights,and increased rural-to-urban migration,with an estimated 281 million international migrants worldwide in 2020,despite COVID-19 movement restrictions.In this review,we synthesized 195 research articles that examined the association between human movement and infectious disease outbreaks to understand the extent to which human mobility has increased the risk of infectious disease outbreaks.This article covers eight infectious diseases,ranging from respiratory illnesses to sexually transmitted and vector-borne diseases.The review revealed a strong association between human mobility and infectious disease spread,particularly strong for respiratory illnesses like COVID-19 and Influenza.Despite significant research into the relationship between infectious diseases and human mobility,four knowledge gaps were identified based on reviewed literature in this study:1)although some studies have used big data in investigating infectious diseases,the efforts are limited(with the exception of COVID-19 disease),2)while some research has explored the use of multiple data sources,there has been limited focus on fully integrating these data into comprehensive analyses,3)limited research on the global impact of mobility on the spread of infectious disease with most studies focusing on local or regional outbreaks,and 4)lack of standardization in the methodology for measuring the impacts of human mobility on infectious disease spread.By tackling the recognized knowledge gaps and adopting holistic,interdisciplinary methods,forthcoming research has the potential to substantially enhance our comprehension of the intricate interplay between human mobility and infectious diseases.
基金supported by National Institutes of Health[grant number 3R01AI127203-04S1]and NSF[grant num-ber 2028791].
文摘Geospatial social media(GSM)data has been increasingly used in public health due to its rich,timely,and accessible spatial information,particularly in infectious disease research.This review synthesized 86 research articles that use GSM data in infectious diseases published between December 2013 and March 2022.These articles cover 12 infectious disease types ranging from respiratory infectious diseases to sexually transmitted diseases with spatial levels varying from the neighborhood,county,state,and country.We categorized these studies into three major infectious disease research domains:surveillance,explanation,and prediction.With the assistance of advanced computing,statistical and spatial methods,GSM data has been widely and deeply applied to these domains,particularly in surveillance and explanation domains.We further identified four knowledge gaps in terms of contextual information use,application scopes,spatiotemporal dimension,and data limitations and proposed innovation opportunities for future research.Ourfindings will contribute to a better understanding of using GSM data in infectious diseases studies and provide insights into strategies for using GSM data more effectively in future research.
