BACKGROUND Although the link between cardiovascular disease(CVD)and various cancers is well-established,the relationship between CVD risk and colorectal cancer(CRC)remains underexplored.AIM To elucidate the relationsh...BACKGROUND Although the link between cardiovascular disease(CVD)and various cancers is well-established,the relationship between CVD risk and colorectal cancer(CRC)remains underexplored.AIM To elucidate the relationship between CVD risk scores and CRC incidence.METHODS In this population-based cohort study,participants from the 2009 National Health Checkup were followed-up until 2020.The cardiovascular(CV)risk score was calculated as the sum of risk factors(age,family history of coronary artery disease,hypertension,smoking status,and high-density lipoprotein levels)with high-density lipoprotein(≥60 mg/dL)reducing the risk score by one.The primary outcome was incidence of newly diagnosed CRC.RESULTS Among 2526628 individuals,30329 developed CRC during a mean follow-up of 10.1 years.Categorized by CV risk scores(0,1,2,and≥3).CRC risk increased with higher CV risk scores after adjusting for covariates[(hazard ratio=1.155,95%confidence interval:1.107-1.205)in risk score≥3,P<0.001].This association individuals not using statins.Moreover,even in participants without diabetes,a higher CV risk was associated with an increased CRC risk.CONCLUSION Increased CV risk scores were significantly associated with higher CRC risk,especially among males,younger populations,and non-statin users.Thus,males with a higher CV risk score,even at a younger age,are recommended to control their risk factors and undergo individualized CRC screening.展开更多
The ability to accurately predict urban traffic flows is crucial for optimising city operations.Consequently,various methods for forecasting urban traffic have been developed,focusing on analysing historical data to u...The ability to accurately predict urban traffic flows is crucial for optimising city operations.Consequently,various methods for forecasting urban traffic have been developed,focusing on analysing historical data to understand complex mobility patterns.Deep learning techniques,such as graph neural networks(GNNs),are popular for their ability to capture spatio-temporal dependencies.However,these models often become overly complex due to the large number of hyper-parameters involved.In this study,we introduce Dynamic Multi-Graph Spatial-Temporal Graph Neural Ordinary Differential Equation Networks(DMST-GNODE),a framework based on ordinary differential equations(ODEs)that autonomously discovers effective spatial-temporal graph neural network(STGNN)architectures for traffic prediction tasks.The comparative analysis of DMST-GNODE and baseline models indicates that DMST-GNODE model demonstrates superior performance across multiple datasets,consistently achieving the lowest Root Mean Square Error(RMSE)and Mean Absolute Error(MAE)values,alongside the highest accuracy.On the BKK(Bangkok)dataset,it outperformed other models with an RMSE of 3.3165 and an accuracy of 0.9367 for a 20-min interval,maintaining this trend across 40 and 60 min.Similarly,on the PeMS08 dataset,DMST-GNODE achieved the best performance with an RMSE of 19.4863 and an accuracy of 0.9377 at 20 min,demonstrating its effectiveness over longer periods.The Los_Loop dataset results further emphasise this model’s advantage,with an RMSE of 3.3422 and an accuracy of 0.7643 at 20 min,consistently maintaining superiority across all time intervals.These numerical highlights indicate that DMST-GNODE not only outperforms baseline models but also achieves higher accuracy and lower errors across different time intervals and datasets.展开更多
Metaheuristics are commonly used in various fields,including real-life problem-solving and engineering applications.The present work introduces a novel metaheuristic algorithm named the Artificial Circulatory System A...Metaheuristics are commonly used in various fields,including real-life problem-solving and engineering applications.The present work introduces a novel metaheuristic algorithm named the Artificial Circulatory System Algorithm(ACSA).The control of the circulatory system inspires it and mimics the behavior of hormonal and neural regulators involved in this process.The work initially evaluates the effectiveness of the suggested approach on 16 two-dimensional test functions,identified as classical benchmark functions.The method was subsequently examined by application to 12 CEC 2022 benchmark problems of different complexities.Furthermore,the paper evaluates ACSA in comparison to 64 metaheuristic methods that are derived from different approaches,including evolutionary,human,physics,and swarm-based.Subsequently,a sequence of statistical tests was undertaken to examine the superiority of the suggested algorithm in comparison to the 7 most widely used algorithms in the existing literature.The results show that the ACSA strategy can quickly reach the global optimum,avoid getting trapped in local optima,and effectively maintain a balance between exploration and exploitation.ACSA outperformed 42 algorithms statistically,according to post-hoc tests.It also outperformed 9 algorithms quantitatively.The study concludes that ACSA offers competitive solutions in comparison to popüler methods.展开更多
BACKGROUND Addressing oculoplastic conditions in the preoperative period ensures both the safety and functional success of any ophthalmic procedure.Some oculoplastic conditions,like nasolacrimal duct obstruction,have ...BACKGROUND Addressing oculoplastic conditions in the preoperative period ensures both the safety and functional success of any ophthalmic procedure.Some oculoplastic conditions,like nasolacrimal duct obstruction,have been extensively studied,whereas others,like eyelid malposition and thyroid eye disease,have received minimal or no research.AIM To investigate the current practice patterns among ophthalmologists while treating concomitant oculoplastic conditions before any subspecialty ophthalmic intervention.METHODS A cross-sectional survey was disseminated among ophthalmologists all over India.The survey included questions related to pre-operative evaluation,anaesthetic and surgical techniques preferred,post-operative care,the use of adjunctive therapies,and patient follow-up patterns.RESULTS A total of 180 ophthalmologists responded to the survey.Most practitioners(89%)felt that the ROPLAS test was sufficient during pre-operative evaluation before any subspecialty surgery was advised.The most common surgical techniques employed were lacrimal drainage procedures(Dacryocystorhinostomy)(63.3%),eyelid malposition repair(36.9%),and ptosis repair(58.7%).Post-operatively,47.7%of respondents emphasized that at least a 4-week gap should be maintained after lacrimal drainage procedures and eyelid surgeries.Sixty-seven percent of ophthalmologists felt that topical anaesthetic procedures should be preferred while performing ocular surgeries in thyroid eye disease patients.CONCLUSION Approximately 50%of ophthalmologists handle prevalent oculoplastic issues themselves,seeking the expertise of an oculoplastic surgeon under particular conditions.Many ophthalmologists still favor using ROPLAS as a preliminary screening method before proceeding with cataract surgery.Eyelid conditions and thyroid eye disease are not as commonly addressed before subspecialty procedures compared to issues like nasolacrimal duct obstruction and periocular infections.展开更多
A Receiver Operating Characteristic(ROC)analysis of a power is important and useful in clinical trials.A Classical Conditional Power(CCP)is a probability of a classical rejection region given values of true treatment ...A Receiver Operating Characteristic(ROC)analysis of a power is important and useful in clinical trials.A Classical Conditional Power(CCP)is a probability of a classical rejection region given values of true treatment effect and interim result.For hypotheses and reversed hypotheses under normal models,we obtain analytical expressions of the ROC curves of the CCP,find optimal ROC curves of the CCP,investigate the superiority of the ROC curves of the CCP,calculate critical values of the False Positive Rate(FPR),True Positive Rate(TPR),and cutoff of the optimal CCP,and give go/no go decisions at the interim of the optimal CCP.In addition,extensive numerical experiments are carried out to exemplify our theoretical results.Finally,a real data example is performed to illustrate the go/no go decisions of the optimal CCP.展开更多
In this study,we examine the problem of sliced inverse regression(SIR),a widely used method for sufficient dimension reduction(SDR).It was designed to find reduced-dimensional versions of multivariate predictors by re...In this study,we examine the problem of sliced inverse regression(SIR),a widely used method for sufficient dimension reduction(SDR).It was designed to find reduced-dimensional versions of multivariate predictors by replacing them with a minimally adequate collection of their linear combinations without loss of information.Recently,regularization methods have been proposed in SIR to incorporate a sparse structure of predictors for better interpretability.However,existing methods consider convex relaxation to bypass the sparsity constraint,which may not lead to the best subset,and particularly tends to include irrelevant variables when predictors are correlated.In this study,we approach sparse SIR as a nonconvex optimization problem and directly tackle the sparsity constraint by establishing the optimal conditions and iteratively solving them by means of the splicing technique.Without employing convex relaxation on the sparsity constraint and the orthogonal constraint,our algorithm exhibits superior empirical merits,as evidenced by extensive numerical studies.Computationally,our algorithm is much faster than the relaxed approach for the natural sparse SIR estimator.Statistically,our algorithm surpasses existing methods in terms of accuracy for central subspace estimation and best subset selection and sustains high performance even with correlated predictors.展开更多
Currently, no clinically approved therapeutic drugs specifically target dengue virus infections. This study aims to evaluate the potential of antiviral drugs originally developed for other purposes as viable candidate...Currently, no clinically approved therapeutic drugs specifically target dengue virus infections. This study aims to evaluate the potential of antiviral drugs originally developed for other purposes as viable candidates for combating dengue virus. The RNA-elongating NS5-NS3 complex is a critical molecular structure responsible for dengue virus replication. Using the cryo-electron microscopy (Cryo-EM) structures available in the Protein Data Bank and AlphaFold 3 predictions, this study simulated the replication complexes of dengue virus serotypes 1, 2, 3, and 4. The RNA-dependent RNA polymerase (RdRp) domain of the NS5 protein within the NS5-NS3 complex was selected as the molecular docking template. Molecular docking simulations were conducted using AutoDock4. Seven small molecules—AT-9010, RK-0404678, Oseltamivir, Remdesivir, Favipiravir-RTP, Abacavir, and Ribavirin—were assessed for binding affinity by calculating their binding energies, where lower values indicate stronger molecular interactions. Based on published data, antiviral replication assays were conducted for the four dengue virus serotypes. AT-9010 and RK-0404678 were used as benchmarks for antiviral replication efficacy, while Oseltamivir served as the control group. The Mann-Whitney U test was employed to classify the clinical antiviral candidates—Remdesivir, Favipiravir-RTP, Abacavir, and Ribavirin. Results demonstrated that among the four small molecules, Favipiravir-RTP exhibited the highest binding affinity with the RdRp domain of the NS5-NS3 complex across all four dengue virus serotypes. Statistical classification revealed that in five simulated scenarios—including the four virus serotypes and Cryo-EM structural data—Favipiravir-RTP shared three classifications with the benchmark molecule AT-9010. Based on these findings, Favipiravir-RTP, a broad-spectrum antiviral agent, shows potential as a therapeutic option for inhibiting dengue virus replication. However, further clinical trials are necessary to validate their efficacy in humans.展开更多
In this present work,we propose the expected Bayesian and hierarchical Bayesian approaches to estimate the shape parameter and hazard rate under a generalized progressive hybrid censoring scheme for the Kumaraswamy di...In this present work,we propose the expected Bayesian and hierarchical Bayesian approaches to estimate the shape parameter and hazard rate under a generalized progressive hybrid censoring scheme for the Kumaraswamy distribution.These estimates have been obtained using gamma priors based on various loss functions such as squared error,entropy,weighted balance,and minimum expected loss functions.An investigation is carried out using Monte Carlo simulation to evaluate the effectiveness of the suggested estimators.The simulation provides a quantitative assessment of the estimates accuracy and efficiency under various conditions by comparing them in terms of mean squared error.Additionally,the monthly water capacity of the Shasta reservoir is examined to offer real-world examples of how the suggested estimations may be used and performed.展开更多
A drought is when reduced rainfall leads to a water crisis,impacting daily life.Over recent decades,droughts have affected various regions,including South Sulawesi,Indonesia.This study aims to map the probability of m...A drought is when reduced rainfall leads to a water crisis,impacting daily life.Over recent decades,droughts have affected various regions,including South Sulawesi,Indonesia.This study aims to map the probability of meteo-rological drought months using the 1-month Standardized Precipitation Index(SPI)in South Sulawesi.Based on SPI,meteorological drought characteristics are inversely proportional to drought event intensity,which can be modeled using a Non-Homogeneous Poisson Process,specifically the Power Law Process.The estimation method employs Maximum Likelihood Estimation(MLE),where drought event intensities are treated as random variables over a set time interval.Future drought months are estimated using the cumulative Power Law Process function,with theβandγparameters more significant than 0.The probability of drought months is determined using the Non-Homogeneous Poisson Process,which models event occurrence over time,considering varying intensities.The results indicate that,of the 24 districts/cities in South Sulawesi,14 experienced meteorological drought based on the SPI and Power Law Process model.The estimated number of months of drought occurrence in the next 12 months is one month of drought with an occurrence probability value of 0.37 occurring in November in the Selayar,Bulukumba,Bantaeng,Jeneponto,Takalar and Gowa areas,in October in the Sinjai,Barru,Bone,Soppeng,Pinrang and Pare-pare areas,as well as in December in the Maros and Makassar areas.展开更多
We introduce a new generalization of the exponentiated power Lindley distribution,called the exponentiated power Lindley power series(EPLPS)distribution.The new distribution arises on a latent complementary risks scen...We introduce a new generalization of the exponentiated power Lindley distribution,called the exponentiated power Lindley power series(EPLPS)distribution.The new distribution arises on a latent complementary risks scenario,in which the lifetime associated with a particular risk is not observable;rather,we observe only the maximum lifetime value among all risks.The distribution exhibits decreasing,increasing,unimodal and bathtub shaped hazard rate functions,depending on its parameters.Several properties of the EPLPS distribution are investigated.Moreover,we discuss maximum likelihood estimation and provide formulas for the elements of the Fisher information matrix.Finally,applications to three real data sets show the flexibility and potentiality of the EPLPS distribution.展开更多
BACKGROUND Understanding a patient's clinical status and setting priorities for their care are two aspects of the constantly changing process of clinical decision-making.One analytical technique that can be helpfu...BACKGROUND Understanding a patient's clinical status and setting priorities for their care are two aspects of the constantly changing process of clinical decision-making.One analytical technique that can be helpful in uncertain situations is clinical judgment.Clinicians must deal with contradictory information,lack of time to make decisions,and long-term factors when emergencies occur.AIM To examine the ethical issues healthcare professionals faced during the coronavirus disease 2019(COVID-19)pandemic and the factors affecting clinical decision-making.METHODS This pilot study,which means it was a preliminary investigation to gather information and test the feasibility of a larger investigation was conducted over 6 months and we invited responses from clinicians worldwide who managed patients with COVID-19.The survey focused on topics related to their professional roles and personal relationships.We examined five core areas influencing critical care decision-making:Patients'personal factors,family-related factors,informed consent,communication and media,and hospital administrative policies on clinical decision-making.The collected data were analyzed using the χ^(2) test for categorical variables.RESULTS A total of 102 clinicians from 23 specialties and 17 countries responded to the survey.Age was a significant factor in treatment planning(n=88)and ventilator access(n=78).Sex had no bearing on how decisions were made.Most doctors reported maintaining patient confidentiality regarding privacy and informed consent.Approximately 50%of clinicians reported a moderate influence of clinical work,with many citing it as one of the most important factors affecting their health and relationships.Clinicians from developing countries had a significantly higher score for considering a patient's financial status when creating a treatment plan than their counterparts from developed countries.Regarding personal experiences,some respondents noted that treatment plans and preferences changed from wave to wave,and that there was a rapid turnover of studies and evidence.Hospital and government policies also played a role in critical decision-making.Rather than assessing the appropriateness of treatment,some doctors observed that hospital policies regarding medications were driven by patient demand.CONCLUSION Factors other than medical considerations frequently affect management choices.The disparity in treatment choices,became more apparent during the pandemic.We highlight the difficulties and contradictions between moral standards and the realities physicians encountered during this medical emergency.False information,large patient populations,and limited resources caused problems for clinicians.These factors impacted decision-making,which,in turn,affected patient care and healthcare staff well-being.展开更多
High-dimensional heterogeneous data have acquired increasing attention and discussion in the past decade.In the context of heterogeneity,semiparametric regression emerges as a popular method to model this type of data...High-dimensional heterogeneous data have acquired increasing attention and discussion in the past decade.In the context of heterogeneity,semiparametric regression emerges as a popular method to model this type of data in statistics.In this paper,we leverage the benefits of expectile regression for computational efficiency and analytical robustness in heterogeneity,and propose a regularized partially linear additive expectile regression model with a nonconvex penalty,such as SCAD or MCP,for high-dimensional heterogeneous data.We focus on a more realistic scenario where the regression error exhibits a heavy-tailed distribution with only finite moments.This scenario challenges the classical sub-gaussian distribution assumption and is more prevalent in practical applications.Under certain regular conditions,we demonstrate that with probability tending to one,the oracle estimator is one of the local minima of the induced optimization problem.Our theoretical analysis suggests that the dimensionality of linear covariates that our estimation procedure can handle is fundamentally limited by the moment condition of the regression error.Computationally,given the nonconvex and nonsmooth nature of the induced optimization problem,we have developed a two-step algorithm.Finally,our method’s effectiveness is demonstrated through its high estimation accuracy and effective model selection,as evidenced by Monte Carlo simulation studies and a real-data application.Furthermore,by taking various expectile weights,our method effectively detects heterogeneity and explores the complete conditional distribution of the response variable,underscoring its utility in analyzing high-dimensional heterogeneous data.展开更多
BACKGROUND Decreased renal function is a well-known risk factor for cardiovascular diseases(CVD)and death.However,the impact of diabetes duration and the glomerular filtration rate(GFR)on cardiovascular complications ...BACKGROUND Decreased renal function is a well-known risk factor for cardiovascular diseases(CVD)and death.However,the impact of diabetes duration and the glomerular filtration rate(GFR)on cardiovascular complications in patients with type 2 dia-betes has not been well studied.AIM To investigate the complex impact of longer diabetes duration and GFR on CVD and mortality.METHODS Subjects with diabetes age≥20 years,who underwent health check-ups from 2015 to 2016 were identified in the Korean National Health Insurance Service database.Based on diabetes duration,subjects were grouped into new-onset,<5 years,5–9 years,or≥10 years.The new-onset diabetes group[estimated GFR(eGFR):≥90 mL/min/1.73 m2]was the reference group.A Cox proportional hazards model adjusted for potential confounders was used to estimate the risk for myocardial infarction(MI),ischemic stroke(IS),and mortality.RESULTS During a 3.9-year follow-up of 2105228 patients,36003(1.7%)MIs,46496(2.2%)ISs,and 73549(3.5%)deaths were documented.Both longer diabetes duration and lower eGFR were independently associated with higher risks of MI,IS,and mortality,which were further amplified when these factors coexisted.Even patients with new-onset diabetes had elevated MI and IS risk at mildly reduced eGFR(60–90 mL/min/1.73 m^(2)).Mortality risk rose appreciably once eGFR declined below 60 mL/min/1.73 m^(2),particularly in those with longer diabetes duration.eGFR≥90 mL/min/1.73 m2 subgroups had higher death risk than eGFR 60–90 mL/min/1.73 m2 subgroups regardless of diabetic duration.CONCLUSION Increasing diabetes duration and decreasing eGFR are associated with increased risk of MI,IS,and mortality.For cardiovascular risk estimation,diabetes duration should be considered an important risk factor.展开更多
1|Introduction Climate change is one of the most significant and widespread global issues,contributing to emerging infectious diseases and threatening human physical and mental health[1,2].Bangladesh,one of the most d...1|Introduction Climate change is one of the most significant and widespread global issues,contributing to emerging infectious diseases and threatening human physical and mental health[1,2].Bangladesh,one of the most densely populated countries in South Asia,experiences unpredictable weather and a steady increase in temperature and precipitation.Between 1901 and 2019,Bangladesh saw an average temperature increase of 0.5℃ according to the change in mean monthly temperatures.Warming is more pronounced in winter months,such as January(from 0.6℃ to 1.9℃)and November(1.3℃ to 1.8℃),compared to smaller increase during the monsoon(Figure 1).This reflects uneven seasonal warming,driven by climate change,with winter and pre-winter months experiencing the most significant temperature rises.展开更多
Biomass models to estimate carbon stocks in arid environment are very limited. This study employed destructive sampling to develop a new biomass model for Vachellia tortilis, a widely known species in the Sultanate of...Biomass models to estimate carbon stocks in arid environment are very limited. This study employed destructive sampling to develop a new biomass model for Vachellia tortilis, a widely known species in the Sultanate of Oman. Twenty trees with a diameter at stump height (DSH) ranging from 18.5 cm to 150 cm were selected based on DSH and height variations for destructive sampling in As Saleel Natural Park Reserve (SNPR) in Al Sharqiyah governorate, South of Oman. Each tree was excavated and cut into three parts: Stems, Branches, twigs, and leaves. The total fresh weight of each tree was obtained in the field using a 300 balance. Sub-samples (250 - 300 grams) were taken from each part of the tree and transferred to the laboratory for dry weight determination. Linear multiple regression analysis was done using SPSS software between the three variables, DSH, H, CA (x) and the total dry biomass (y). Five models were tested for the best-fit model based on R-Square and Mean Square Error (MSE). Model 5 was the best-fit model, including the LOG of DSH and the LOG of CA (R2 = 0.97, MSE = 0.114). The models developed in this research fill a critical gap in estimating the AGB of terrestrial native species in Oman and other countries with similar ecological and climate conditions.展开更多
The integration of artificial intelligence(AI)into various sectors has undoubtedly brought about numerous benefits,from increased efficiency to innovative problem‐solving.The growing influence of AI across several in...The integration of artificial intelligence(AI)into various sectors has undoubtedly brought about numerous benefits,from increased efficiency to innovative problem‐solving.The growing influence of AI across several industries may help to achieve the sustainable development goals(SDGs).However,due to the AI revolution happening in industries across the globe,older employees are often confronted with significant hurdles in keeping pace with these changes.The threat of job displacement looms large as automation driven by AI encroaches upon routine tasks previously performed by human workers.Job insecurity,that is,worry of losing one's job encompasses anxiety,and uneasiness,and affects the mental health of employees.To address these challenges and empower older employees in the era of open AI,it is imperative that organizations implement targeted strategies tailored to their unique needs and circumstances.Employees use the opportunities for continued education provided to them with company support to prevent unwanted effects.organizations can create an inclusive and supportive environment where older employees are empowered to embrace the opportunities presented by AI while leveraging their experience and expertise to drive innovation and success.展开更多
This study introduces an innovative computational framework leveraging the transformer architecture to address a critical challenge in chemical process engineering:predicting and optimizing light olefin yields in indu...This study introduces an innovative computational framework leveraging the transformer architecture to address a critical challenge in chemical process engineering:predicting and optimizing light olefin yields in industrial methanol-to-olefins(MTO)processes.Our approach integrates advanced machine learning techniques with chemical engineering principles to tackle the complexities of non-stationary,highly volatile production data in large-scale chemical manufacturing.The framework employs the maximal information coefficient(MIC)algorithm to analyze and select the significant variables from MTO process parameters,forming a robust dataset for model development.We implement a transformer-based time series forecasting model,enhanced through positional encoding and hyperparameter optimization,significantly improving predictive accuracy for ethylene and propylene yields.The model's interpretability is augmented by applying SHapley additive exPlanations(SHAP)to quantify and visualize the impact of reaction control variables on olefin yields,providing valuable insights for process optimization.Experimental results demonstrate that our model outperforms traditional statistical and machine learning methods in accuracy and interpretability,effectively handling nonlinear,non-stationary,highvolatility,and long-sequence data challenges in olefin yield prediction.This research contributes to chemical engineering by providing a novel computerized methodology for solving complex production optimization problems in the chemical industry,offering significant potential for enhancing decisionmaking in MTO system production control and fostering the intelligent transformation of manufacturing processes.展开更多
Sonic Hedgehog Medulloblastoma(SHH-MB)is one of the four primary molecular subgroups of Medulloblastoma.It is estimated to be responsible for nearly one-third of allMB cases.Using transcriptomic and DNA methylation pr...Sonic Hedgehog Medulloblastoma(SHH-MB)is one of the four primary molecular subgroups of Medulloblastoma.It is estimated to be responsible for nearly one-third of allMB cases.Using transcriptomic and DNA methylation profiling techniques,new developments in this field determined four molecular subtypes for SHH-MB.SHH-MB subtypes show distinct DNAmethylation patterns that allow their discrimination fromoverlapping subtypes and predict clinical outcomes.Class overlapping occurs when two or more classes share common features,making it difficult to distinguish them as separate.Using the DNA methylation dataset,a novel classification technique is presented to address the issue of overlapping SHH-MBsubtypes.Penalizedmultinomial regression(PMR),Tomek links(TL),and singular value decomposition(SVD)were all smoothly integrated into a single framework.SVD and group lasso improve computational efficiency,address the problem of high-dimensional datasets,and clarify class distinctions by removing redundant or irrelevant features that might lead to class overlap.As a method to eliminate the issues of decision boundary overlap and class imbalance in the classification task,TL enhances dataset balance and increases the clarity of decision boundaries through the elimination of overlapping samples.Using fivefold cross-validation,our proposed method(TL-SVDPMR)achieved a remarkable overall accuracy of almost 95%in the classification of SHH-MB molecular subtypes.The results demonstrate the strong performance of the proposed classification model among the various SHH-MB subtypes given a high average of the area under the curve(AUC)values.Additionally,the statistical significance test indicates that TL-SVDPMR is more accurate than both SVM and random forest algorithms in classifying the overlapping SHH-MB subtypes,highlighting its importance for precision medicine applications.Our findings emphasized the success of combining SVD,TL,and PMRtechniques to improve the classification performance for biomedical applications with many features and overlapping subtypes.展开更多
Parametric survival models are essential for analyzing time-to-event data in fields such as engineering and biomedicine.While the log-logistic distribution is popular for its simplicity and closed-form expressions,it ...Parametric survival models are essential for analyzing time-to-event data in fields such as engineering and biomedicine.While the log-logistic distribution is popular for its simplicity and closed-form expressions,it often lacks the flexibility needed to capture complex hazard patterns.In this article,we propose a novel extension of the classical log-logistic distribution,termed the new exponential log-logistic(NExLL)distribution,designed to provide enhanced flexibility in modeling time-to-event data with complex failure behaviors.The NExLL model incorporates a new exponential generator to expand the shape adaptability of the baseline log-logistic distribution,allowing it to capture a wide range of hazard rate shapes,including increasing,decreasing,J-shaped,reversed J-shaped,modified bathtub,and unimodal forms.A key feature of the NExLL distribution is its formulation as a mixture of log-logistic densities,offering both symmetric and asymmetric patterns suitable for diverse real-world reliability scenarios.We establish several theoretical properties of the model,including closed-form expressions for its probability density function,cumulative distribution function,moments,hazard rate function,and quantiles.Parameter estimation is performed using seven classical estimation techniques,with extensive Monte Carlo simulations used to evaluate and compare their performance under various conditions.The practical utility and flexibility of the proposed model are illustrated using two real-world datasets from reliability and engineering applications,where the NExLL model demonstrates superior fit and predictive performance compared to existing log-logistic-basedmodels.This contribution advances the toolbox of parametric survivalmodels,offering a robust alternative formodeling complex aging and failure patterns in reliability,engineering,and other applied domains.展开更多
BACKGROUND Exercise plays a key role in managing chronic conditions such as diabetes mellitus(DM),a major contributor to end-stage renal disease(ESRD),a serious public health issue.AIM To investigate the relationship ...BACKGROUND Exercise plays a key role in managing chronic conditions such as diabetes mellitus(DM),a major contributor to end-stage renal disease(ESRD),a serious public health issue.AIM To investigate the relationship between exercise intensity,DM duration,and ESRD incidence.METHODS This retrospective cohort study analyzed data from 2495031 individuals with DM who underwent the Korean National Health Screening between 2015 and 2016,with follow-up through 2022.The Cox proportional hazards model was adjusted for confounders,including age,sex,income,smoking,and baseline comorbidities.RESULTS Longer DM duration was associated with a significantly higher risk of ESRD,with durations≥10 years showing the highest risk[hazard ratio(HR):2.624,95%confidence interval(CI):2.486-2.770].Increased exercise intensity reduced the risk of developing ESRD across all diabetes duration groups,with the highest exercise category(≥1500 metabolic equivalents of task-min/week)demonstrating a protective effect compared to that of no exercise(HR:0.837,95%CI:0.791-0.886).Exercise benefits were more pronounced in patients without hypertension,non-smokers,and those with lower alcohol consumption.Additionally,ESRD risk reduction was significant among patients with a body mass index≥25 and those without proteinuria or chronic kidney disease.CONCLUSION Longer diabetes duration is associated with increased ESRD risk,while high-intensity exercise may mitigate this risk.These findings suggest promoting exercise is important for managing diabetes to reduce renal complications.展开更多
文摘BACKGROUND Although the link between cardiovascular disease(CVD)and various cancers is well-established,the relationship between CVD risk and colorectal cancer(CRC)remains underexplored.AIM To elucidate the relationship between CVD risk scores and CRC incidence.METHODS In this population-based cohort study,participants from the 2009 National Health Checkup were followed-up until 2020.The cardiovascular(CV)risk score was calculated as the sum of risk factors(age,family history of coronary artery disease,hypertension,smoking status,and high-density lipoprotein levels)with high-density lipoprotein(≥60 mg/dL)reducing the risk score by one.The primary outcome was incidence of newly diagnosed CRC.RESULTS Among 2526628 individuals,30329 developed CRC during a mean follow-up of 10.1 years.Categorized by CV risk scores(0,1,2,and≥3).CRC risk increased with higher CV risk scores after adjusting for covariates[(hazard ratio=1.155,95%confidence interval:1.107-1.205)in risk score≥3,P<0.001].This association individuals not using statins.Moreover,even in participants without diabetes,a higher CV risk was associated with an increased CRC risk.CONCLUSION Increased CV risk scores were significantly associated with higher CRC risk,especially among males,younger populations,and non-statin users.Thus,males with a higher CV risk score,even at a younger age,are recommended to control their risk factors and undergo individualized CRC screening.
文摘The ability to accurately predict urban traffic flows is crucial for optimising city operations.Consequently,various methods for forecasting urban traffic have been developed,focusing on analysing historical data to understand complex mobility patterns.Deep learning techniques,such as graph neural networks(GNNs),are popular for their ability to capture spatio-temporal dependencies.However,these models often become overly complex due to the large number of hyper-parameters involved.In this study,we introduce Dynamic Multi-Graph Spatial-Temporal Graph Neural Ordinary Differential Equation Networks(DMST-GNODE),a framework based on ordinary differential equations(ODEs)that autonomously discovers effective spatial-temporal graph neural network(STGNN)architectures for traffic prediction tasks.The comparative analysis of DMST-GNODE and baseline models indicates that DMST-GNODE model demonstrates superior performance across multiple datasets,consistently achieving the lowest Root Mean Square Error(RMSE)and Mean Absolute Error(MAE)values,alongside the highest accuracy.On the BKK(Bangkok)dataset,it outperformed other models with an RMSE of 3.3165 and an accuracy of 0.9367 for a 20-min interval,maintaining this trend across 40 and 60 min.Similarly,on the PeMS08 dataset,DMST-GNODE achieved the best performance with an RMSE of 19.4863 and an accuracy of 0.9377 at 20 min,demonstrating its effectiveness over longer periods.The Los_Loop dataset results further emphasise this model’s advantage,with an RMSE of 3.3422 and an accuracy of 0.7643 at 20 min,consistently maintaining superiority across all time intervals.These numerical highlights indicate that DMST-GNODE not only outperforms baseline models but also achieves higher accuracy and lower errors across different time intervals and datasets.
文摘Metaheuristics are commonly used in various fields,including real-life problem-solving and engineering applications.The present work introduces a novel metaheuristic algorithm named the Artificial Circulatory System Algorithm(ACSA).The control of the circulatory system inspires it and mimics the behavior of hormonal and neural regulators involved in this process.The work initially evaluates the effectiveness of the suggested approach on 16 two-dimensional test functions,identified as classical benchmark functions.The method was subsequently examined by application to 12 CEC 2022 benchmark problems of different complexities.Furthermore,the paper evaluates ACSA in comparison to 64 metaheuristic methods that are derived from different approaches,including evolutionary,human,physics,and swarm-based.Subsequently,a sequence of statistical tests was undertaken to examine the superiority of the suggested algorithm in comparison to the 7 most widely used algorithms in the existing literature.The results show that the ACSA strategy can quickly reach the global optimum,avoid getting trapped in local optima,and effectively maintain a balance between exploration and exploitation.ACSA outperformed 42 algorithms statistically,according to post-hoc tests.It also outperformed 9 algorithms quantitatively.The study concludes that ACSA offers competitive solutions in comparison to popüler methods.
文摘BACKGROUND Addressing oculoplastic conditions in the preoperative period ensures both the safety and functional success of any ophthalmic procedure.Some oculoplastic conditions,like nasolacrimal duct obstruction,have been extensively studied,whereas others,like eyelid malposition and thyroid eye disease,have received minimal or no research.AIM To investigate the current practice patterns among ophthalmologists while treating concomitant oculoplastic conditions before any subspecialty ophthalmic intervention.METHODS A cross-sectional survey was disseminated among ophthalmologists all over India.The survey included questions related to pre-operative evaluation,anaesthetic and surgical techniques preferred,post-operative care,the use of adjunctive therapies,and patient follow-up patterns.RESULTS A total of 180 ophthalmologists responded to the survey.Most practitioners(89%)felt that the ROPLAS test was sufficient during pre-operative evaluation before any subspecialty surgery was advised.The most common surgical techniques employed were lacrimal drainage procedures(Dacryocystorhinostomy)(63.3%),eyelid malposition repair(36.9%),and ptosis repair(58.7%).Post-operatively,47.7%of respondents emphasized that at least a 4-week gap should be maintained after lacrimal drainage procedures and eyelid surgeries.Sixty-seven percent of ophthalmologists felt that topical anaesthetic procedures should be preferred while performing ocular surgeries in thyroid eye disease patients.CONCLUSION Approximately 50%of ophthalmologists handle prevalent oculoplastic issues themselves,seeking the expertise of an oculoplastic surgeon under particular conditions.Many ophthalmologists still favor using ROPLAS as a preliminary screening method before proceeding with cataract surgery.Eyelid conditions and thyroid eye disease are not as commonly addressed before subspecialty procedures compared to issues like nasolacrimal duct obstruction and periocular infections.
基金supported by the National Social Science Fund of China(Grand No.21XTJ001).
文摘A Receiver Operating Characteristic(ROC)analysis of a power is important and useful in clinical trials.A Classical Conditional Power(CCP)is a probability of a classical rejection region given values of true treatment effect and interim result.For hypotheses and reversed hypotheses under normal models,we obtain analytical expressions of the ROC curves of the CCP,find optimal ROC curves of the CCP,investigate the superiority of the ROC curves of the CCP,calculate critical values of the False Positive Rate(FPR),True Positive Rate(TPR),and cutoff of the optimal CCP,and give go/no go decisions at the interim of the optimal CCP.In addition,extensive numerical experiments are carried out to exemplify our theoretical results.Finally,a real data example is performed to illustrate the go/no go decisions of the optimal CCP.
文摘In this study,we examine the problem of sliced inverse regression(SIR),a widely used method for sufficient dimension reduction(SDR).It was designed to find reduced-dimensional versions of multivariate predictors by replacing them with a minimally adequate collection of their linear combinations without loss of information.Recently,regularization methods have been proposed in SIR to incorporate a sparse structure of predictors for better interpretability.However,existing methods consider convex relaxation to bypass the sparsity constraint,which may not lead to the best subset,and particularly tends to include irrelevant variables when predictors are correlated.In this study,we approach sparse SIR as a nonconvex optimization problem and directly tackle the sparsity constraint by establishing the optimal conditions and iteratively solving them by means of the splicing technique.Without employing convex relaxation on the sparsity constraint and the orthogonal constraint,our algorithm exhibits superior empirical merits,as evidenced by extensive numerical studies.Computationally,our algorithm is much faster than the relaxed approach for the natural sparse SIR estimator.Statistically,our algorithm surpasses existing methods in terms of accuracy for central subspace estimation and best subset selection and sustains high performance even with correlated predictors.
文摘Currently, no clinically approved therapeutic drugs specifically target dengue virus infections. This study aims to evaluate the potential of antiviral drugs originally developed for other purposes as viable candidates for combating dengue virus. The RNA-elongating NS5-NS3 complex is a critical molecular structure responsible for dengue virus replication. Using the cryo-electron microscopy (Cryo-EM) structures available in the Protein Data Bank and AlphaFold 3 predictions, this study simulated the replication complexes of dengue virus serotypes 1, 2, 3, and 4. The RNA-dependent RNA polymerase (RdRp) domain of the NS5 protein within the NS5-NS3 complex was selected as the molecular docking template. Molecular docking simulations were conducted using AutoDock4. Seven small molecules—AT-9010, RK-0404678, Oseltamivir, Remdesivir, Favipiravir-RTP, Abacavir, and Ribavirin—were assessed for binding affinity by calculating their binding energies, where lower values indicate stronger molecular interactions. Based on published data, antiviral replication assays were conducted for the four dengue virus serotypes. AT-9010 and RK-0404678 were used as benchmarks for antiviral replication efficacy, while Oseltamivir served as the control group. The Mann-Whitney U test was employed to classify the clinical antiviral candidates—Remdesivir, Favipiravir-RTP, Abacavir, and Ribavirin. Results demonstrated that among the four small molecules, Favipiravir-RTP exhibited the highest binding affinity with the RdRp domain of the NS5-NS3 complex across all four dengue virus serotypes. Statistical classification revealed that in five simulated scenarios—including the four virus serotypes and Cryo-EM structural data—Favipiravir-RTP shared three classifications with the benchmark molecule AT-9010. Based on these findings, Favipiravir-RTP, a broad-spectrum antiviral agent, shows potential as a therapeutic option for inhibiting dengue virus replication. However, further clinical trials are necessary to validate their efficacy in humans.
基金funded by Researchers Supporting Project number(RSPD2025R969),King Saud University,Riyadh,Saudi Arabia.
文摘In this present work,we propose the expected Bayesian and hierarchical Bayesian approaches to estimate the shape parameter and hazard rate under a generalized progressive hybrid censoring scheme for the Kumaraswamy distribution.These estimates have been obtained using gamma priors based on various loss functions such as squared error,entropy,weighted balance,and minimum expected loss functions.An investigation is carried out using Monte Carlo simulation to evaluate the effectiveness of the suggested estimators.The simulation provides a quantitative assessment of the estimates accuracy and efficiency under various conditions by comparing them in terms of mean squared error.Additionally,the monthly water capacity of the Shasta reservoir is examined to offer real-world examples of how the suggested estimations may be used and performed.
基金funded by Hasanuddin University,grant number 00309/UN4.22/PT.01.03/2024.
文摘A drought is when reduced rainfall leads to a water crisis,impacting daily life.Over recent decades,droughts have affected various regions,including South Sulawesi,Indonesia.This study aims to map the probability of meteo-rological drought months using the 1-month Standardized Precipitation Index(SPI)in South Sulawesi.Based on SPI,meteorological drought characteristics are inversely proportional to drought event intensity,which can be modeled using a Non-Homogeneous Poisson Process,specifically the Power Law Process.The estimation method employs Maximum Likelihood Estimation(MLE),where drought event intensities are treated as random variables over a set time interval.Future drought months are estimated using the cumulative Power Law Process function,with theβandγparameters more significant than 0.The probability of drought months is determined using the Non-Homogeneous Poisson Process,which models event occurrence over time,considering varying intensities.The results indicate that,of the 24 districts/cities in South Sulawesi,14 experienced meteorological drought based on the SPI and Power Law Process model.The estimated number of months of drought occurrence in the next 12 months is one month of drought with an occurrence probability value of 0.37 occurring in November in the Selayar,Bulukumba,Bantaeng,Jeneponto,Takalar and Gowa areas,in October in the Sinjai,Barru,Bone,Soppeng,Pinrang and Pare-pare areas,as well as in December in the Maros and Makassar areas.
文摘We introduce a new generalization of the exponentiated power Lindley distribution,called the exponentiated power Lindley power series(EPLPS)distribution.The new distribution arises on a latent complementary risks scenario,in which the lifetime associated with a particular risk is not observable;rather,we observe only the maximum lifetime value among all risks.The distribution exhibits decreasing,increasing,unimodal and bathtub shaped hazard rate functions,depending on its parameters.Several properties of the EPLPS distribution are investigated.Moreover,we discuss maximum likelihood estimation and provide formulas for the elements of the Fisher information matrix.Finally,applications to three real data sets show the flexibility and potentiality of the EPLPS distribution.
文摘BACKGROUND Understanding a patient's clinical status and setting priorities for their care are two aspects of the constantly changing process of clinical decision-making.One analytical technique that can be helpful in uncertain situations is clinical judgment.Clinicians must deal with contradictory information,lack of time to make decisions,and long-term factors when emergencies occur.AIM To examine the ethical issues healthcare professionals faced during the coronavirus disease 2019(COVID-19)pandemic and the factors affecting clinical decision-making.METHODS This pilot study,which means it was a preliminary investigation to gather information and test the feasibility of a larger investigation was conducted over 6 months and we invited responses from clinicians worldwide who managed patients with COVID-19.The survey focused on topics related to their professional roles and personal relationships.We examined five core areas influencing critical care decision-making:Patients'personal factors,family-related factors,informed consent,communication and media,and hospital administrative policies on clinical decision-making.The collected data were analyzed using the χ^(2) test for categorical variables.RESULTS A total of 102 clinicians from 23 specialties and 17 countries responded to the survey.Age was a significant factor in treatment planning(n=88)and ventilator access(n=78).Sex had no bearing on how decisions were made.Most doctors reported maintaining patient confidentiality regarding privacy and informed consent.Approximately 50%of clinicians reported a moderate influence of clinical work,with many citing it as one of the most important factors affecting their health and relationships.Clinicians from developing countries had a significantly higher score for considering a patient's financial status when creating a treatment plan than their counterparts from developed countries.Regarding personal experiences,some respondents noted that treatment plans and preferences changed from wave to wave,and that there was a rapid turnover of studies and evidence.Hospital and government policies also played a role in critical decision-making.Rather than assessing the appropriateness of treatment,some doctors observed that hospital policies regarding medications were driven by patient demand.CONCLUSION Factors other than medical considerations frequently affect management choices.The disparity in treatment choices,became more apparent during the pandemic.We highlight the difficulties and contradictions between moral standards and the realities physicians encountered during this medical emergency.False information,large patient populations,and limited resources caused problems for clinicians.These factors impacted decision-making,which,in turn,affected patient care and healthcare staff well-being.
基金Supported by the Hangzhou Joint Fund of the Zhejiang Provincial Natural Science Foundation of Chi-na(LHZY24A010002)the MOE Project of Humanities and Social Sciences(21YJCZH235).
文摘High-dimensional heterogeneous data have acquired increasing attention and discussion in the past decade.In the context of heterogeneity,semiparametric regression emerges as a popular method to model this type of data in statistics.In this paper,we leverage the benefits of expectile regression for computational efficiency and analytical robustness in heterogeneity,and propose a regularized partially linear additive expectile regression model with a nonconvex penalty,such as SCAD or MCP,for high-dimensional heterogeneous data.We focus on a more realistic scenario where the regression error exhibits a heavy-tailed distribution with only finite moments.This scenario challenges the classical sub-gaussian distribution assumption and is more prevalent in practical applications.Under certain regular conditions,we demonstrate that with probability tending to one,the oracle estimator is one of the local minima of the induced optimization problem.Our theoretical analysis suggests that the dimensionality of linear covariates that our estimation procedure can handle is fundamentally limited by the moment condition of the regression error.Computationally,given the nonconvex and nonsmooth nature of the induced optimization problem,we have developed a two-step algorithm.Finally,our method’s effectiveness is demonstrated through its high estimation accuracy and effective model selection,as evidenced by Monte Carlo simulation studies and a real-data application.Furthermore,by taking various expectile weights,our method effectively detects heterogeneity and explores the complete conditional distribution of the response variable,underscoring its utility in analyzing high-dimensional heterogeneous data.
基金Supported by the National Research Foundation of Korea grant funded by the Korea government,No.RS-2023-00217317the Korea Health Technology R and D Project through the Korea Health Industry Development Institute funded by the Ministry of Health and Welfare,Republic of Korea,No.RS-2024-00439029.
文摘BACKGROUND Decreased renal function is a well-known risk factor for cardiovascular diseases(CVD)and death.However,the impact of diabetes duration and the glomerular filtration rate(GFR)on cardiovascular complications in patients with type 2 dia-betes has not been well studied.AIM To investigate the complex impact of longer diabetes duration and GFR on CVD and mortality.METHODS Subjects with diabetes age≥20 years,who underwent health check-ups from 2015 to 2016 were identified in the Korean National Health Insurance Service database.Based on diabetes duration,subjects were grouped into new-onset,<5 years,5–9 years,or≥10 years.The new-onset diabetes group[estimated GFR(eGFR):≥90 mL/min/1.73 m2]was the reference group.A Cox proportional hazards model adjusted for potential confounders was used to estimate the risk for myocardial infarction(MI),ischemic stroke(IS),and mortality.RESULTS During a 3.9-year follow-up of 2105228 patients,36003(1.7%)MIs,46496(2.2%)ISs,and 73549(3.5%)deaths were documented.Both longer diabetes duration and lower eGFR were independently associated with higher risks of MI,IS,and mortality,which were further amplified when these factors coexisted.Even patients with new-onset diabetes had elevated MI and IS risk at mildly reduced eGFR(60–90 mL/min/1.73 m^(2)).Mortality risk rose appreciably once eGFR declined below 60 mL/min/1.73 m^(2),particularly in those with longer diabetes duration.eGFR≥90 mL/min/1.73 m2 subgroups had higher death risk than eGFR 60–90 mL/min/1.73 m2 subgroups regardless of diabetic duration.CONCLUSION Increasing diabetes duration and decreasing eGFR are associated with increased risk of MI,IS,and mortality.For cardiovascular risk estimation,diabetes duration should be considered an important risk factor.
文摘1|Introduction Climate change is one of the most significant and widespread global issues,contributing to emerging infectious diseases and threatening human physical and mental health[1,2].Bangladesh,one of the most densely populated countries in South Asia,experiences unpredictable weather and a steady increase in temperature and precipitation.Between 1901 and 2019,Bangladesh saw an average temperature increase of 0.5℃ according to the change in mean monthly temperatures.Warming is more pronounced in winter months,such as January(from 0.6℃ to 1.9℃)and November(1.3℃ to 1.8℃),compared to smaller increase during the monsoon(Figure 1).This reflects uneven seasonal warming,driven by climate change,with winter and pre-winter months experiencing the most significant temperature rises.
文摘Biomass models to estimate carbon stocks in arid environment are very limited. This study employed destructive sampling to develop a new biomass model for Vachellia tortilis, a widely known species in the Sultanate of Oman. Twenty trees with a diameter at stump height (DSH) ranging from 18.5 cm to 150 cm were selected based on DSH and height variations for destructive sampling in As Saleel Natural Park Reserve (SNPR) in Al Sharqiyah governorate, South of Oman. Each tree was excavated and cut into three parts: Stems, Branches, twigs, and leaves. The total fresh weight of each tree was obtained in the field using a 300 balance. Sub-samples (250 - 300 grams) were taken from each part of the tree and transferred to the laboratory for dry weight determination. Linear multiple regression analysis was done using SPSS software between the three variables, DSH, H, CA (x) and the total dry biomass (y). Five models were tested for the best-fit model based on R-Square and Mean Square Error (MSE). Model 5 was the best-fit model, including the LOG of DSH and the LOG of CA (R2 = 0.97, MSE = 0.114). The models developed in this research fill a critical gap in estimating the AGB of terrestrial native species in Oman and other countries with similar ecological and climate conditions.
文摘The integration of artificial intelligence(AI)into various sectors has undoubtedly brought about numerous benefits,from increased efficiency to innovative problem‐solving.The growing influence of AI across several industries may help to achieve the sustainable development goals(SDGs).However,due to the AI revolution happening in industries across the globe,older employees are often confronted with significant hurdles in keeping pace with these changes.The threat of job displacement looms large as automation driven by AI encroaches upon routine tasks previously performed by human workers.Job insecurity,that is,worry of losing one's job encompasses anxiety,and uneasiness,and affects the mental health of employees.To address these challenges and empower older employees in the era of open AI,it is imperative that organizations implement targeted strategies tailored to their unique needs and circumstances.Employees use the opportunities for continued education provided to them with company support to prevent unwanted effects.organizations can create an inclusive and supportive environment where older employees are empowered to embrace the opportunities presented by AI while leveraging their experience and expertise to drive innovation and success.
基金supported by the Humanities and Social Sciences Foundation of the Ministry of Education(22YJC910011)the China Postdoctoral Science Foundation(2023M733444)the Key Research and Development Program in Artificial Intelligence of Liaoning Province(2023JH26/10200012).
文摘This study introduces an innovative computational framework leveraging the transformer architecture to address a critical challenge in chemical process engineering:predicting and optimizing light olefin yields in industrial methanol-to-olefins(MTO)processes.Our approach integrates advanced machine learning techniques with chemical engineering principles to tackle the complexities of non-stationary,highly volatile production data in large-scale chemical manufacturing.The framework employs the maximal information coefficient(MIC)algorithm to analyze and select the significant variables from MTO process parameters,forming a robust dataset for model development.We implement a transformer-based time series forecasting model,enhanced through positional encoding and hyperparameter optimization,significantly improving predictive accuracy for ethylene and propylene yields.The model's interpretability is augmented by applying SHapley additive exPlanations(SHAP)to quantify and visualize the impact of reaction control variables on olefin yields,providing valuable insights for process optimization.Experimental results demonstrate that our model outperforms traditional statistical and machine learning methods in accuracy and interpretability,effectively handling nonlinear,non-stationary,highvolatility,and long-sequence data challenges in olefin yield prediction.This research contributes to chemical engineering by providing a novel computerized methodology for solving complex production optimization problems in the chemical industry,offering significant potential for enhancing decisionmaking in MTO system production control and fostering the intelligent transformation of manufacturing processes.
基金funded by the Deanship of Graduate Studies and Scientific Research at Jouf University under grant No.(DGSSR-2024-02-01137).
文摘Sonic Hedgehog Medulloblastoma(SHH-MB)is one of the four primary molecular subgroups of Medulloblastoma.It is estimated to be responsible for nearly one-third of allMB cases.Using transcriptomic and DNA methylation profiling techniques,new developments in this field determined four molecular subtypes for SHH-MB.SHH-MB subtypes show distinct DNAmethylation patterns that allow their discrimination fromoverlapping subtypes and predict clinical outcomes.Class overlapping occurs when two or more classes share common features,making it difficult to distinguish them as separate.Using the DNA methylation dataset,a novel classification technique is presented to address the issue of overlapping SHH-MBsubtypes.Penalizedmultinomial regression(PMR),Tomek links(TL),and singular value decomposition(SVD)were all smoothly integrated into a single framework.SVD and group lasso improve computational efficiency,address the problem of high-dimensional datasets,and clarify class distinctions by removing redundant or irrelevant features that might lead to class overlap.As a method to eliminate the issues of decision boundary overlap and class imbalance in the classification task,TL enhances dataset balance and increases the clarity of decision boundaries through the elimination of overlapping samples.Using fivefold cross-validation,our proposed method(TL-SVDPMR)achieved a remarkable overall accuracy of almost 95%in the classification of SHH-MB molecular subtypes.The results demonstrate the strong performance of the proposed classification model among the various SHH-MB subtypes given a high average of the area under the curve(AUC)values.Additionally,the statistical significance test indicates that TL-SVDPMR is more accurate than both SVM and random forest algorithms in classifying the overlapping SHH-MB subtypes,highlighting its importance for precision medicine applications.Our findings emphasized the success of combining SVD,TL,and PMRtechniques to improve the classification performance for biomedical applications with many features and overlapping subtypes.
文摘Parametric survival models are essential for analyzing time-to-event data in fields such as engineering and biomedicine.While the log-logistic distribution is popular for its simplicity and closed-form expressions,it often lacks the flexibility needed to capture complex hazard patterns.In this article,we propose a novel extension of the classical log-logistic distribution,termed the new exponential log-logistic(NExLL)distribution,designed to provide enhanced flexibility in modeling time-to-event data with complex failure behaviors.The NExLL model incorporates a new exponential generator to expand the shape adaptability of the baseline log-logistic distribution,allowing it to capture a wide range of hazard rate shapes,including increasing,decreasing,J-shaped,reversed J-shaped,modified bathtub,and unimodal forms.A key feature of the NExLL distribution is its formulation as a mixture of log-logistic densities,offering both symmetric and asymmetric patterns suitable for diverse real-world reliability scenarios.We establish several theoretical properties of the model,including closed-form expressions for its probability density function,cumulative distribution function,moments,hazard rate function,and quantiles.Parameter estimation is performed using seven classical estimation techniques,with extensive Monte Carlo simulations used to evaluate and compare their performance under various conditions.The practical utility and flexibility of the proposed model are illustrated using two real-world datasets from reliability and engineering applications,where the NExLL model demonstrates superior fit and predictive performance compared to existing log-logistic-basedmodels.This contribution advances the toolbox of parametric survivalmodels,offering a robust alternative formodeling complex aging and failure patterns in reliability,engineering,and other applied domains.
基金Supported by National Research Foundation of Korea(NRF)grant funded by the Korean Government(MSIT),No.RS-2023-00217317Chonnam National University Grant,No.2024-0444-01and Chonnam National University Hospital Institute for Biomedical Science,No.BCRI24032.
文摘BACKGROUND Exercise plays a key role in managing chronic conditions such as diabetes mellitus(DM),a major contributor to end-stage renal disease(ESRD),a serious public health issue.AIM To investigate the relationship between exercise intensity,DM duration,and ESRD incidence.METHODS This retrospective cohort study analyzed data from 2495031 individuals with DM who underwent the Korean National Health Screening between 2015 and 2016,with follow-up through 2022.The Cox proportional hazards model was adjusted for confounders,including age,sex,income,smoking,and baseline comorbidities.RESULTS Longer DM duration was associated with a significantly higher risk of ESRD,with durations≥10 years showing the highest risk[hazard ratio(HR):2.624,95%confidence interval(CI):2.486-2.770].Increased exercise intensity reduced the risk of developing ESRD across all diabetes duration groups,with the highest exercise category(≥1500 metabolic equivalents of task-min/week)demonstrating a protective effect compared to that of no exercise(HR:0.837,95%CI:0.791-0.886).Exercise benefits were more pronounced in patients without hypertension,non-smokers,and those with lower alcohol consumption.Additionally,ESRD risk reduction was significant among patients with a body mass index≥25 and those without proteinuria or chronic kidney disease.CONCLUSION Longer diabetes duration is associated with increased ESRD risk,while high-intensity exercise may mitigate this risk.These findings suggest promoting exercise is important for managing diabetes to reduce renal complications.
