This study proposes a novel forecasting framework that simultaneously captures the strong periodicity and irregular meteorological fluctuations inherent in solar radiation time series.Existing approaches typically def...This study proposes a novel forecasting framework that simultaneously captures the strong periodicity and irregular meteorological fluctuations inherent in solar radiation time series.Existing approaches typically define inter-regional correlations using either simple correlation coefficients or distance-based measures when applying spatio-temporal graph neural networks(STGNNs).However,such definitions are prone to generating spurious correlations due to the dominance of periodic structures.To address this limitation,we adopt the Elastic-Band Transform(EBT)to decompose solar radiation into periodic and amplitude-modulated components,which are then modeled independently with separate graph neural networks.The periodic component,characterized by strong nationwide correlations,is learned with a relatively simple architecture,whereas the amplitude-modulated component is modeled with more complex STGNNs that capture climatological similarities between regions.The predictions from the two components are subsequently recombined to yield final forecasts that integrate both periodic patterns and aperiodic variability.The proposed framework is validated with multiple STGNN architectures,and experimental results demonstrate improved predictive accuracy and interpretability compared to conventional methods.展开更多
Traditional anomaly detection methods often assume that data points are independent or exhibit regularly structured relationships,as in Euclidean data such as time series or image grids.However,real-world data frequen...Traditional anomaly detection methods often assume that data points are independent or exhibit regularly structured relationships,as in Euclidean data such as time series or image grids.However,real-world data frequently involve irregular,interconnected structures,requiring a shift toward non-Euclidean approaches.This study introduces a novel anomaly detection framework designed to handle non-Euclidean data by modeling transactions as graph signals.By leveraging graph convolution filters,we extract meaningful connection strengths that capture relational dependencies often overlooked in traditional methods.Utilizing the Graph Convolutional Networks(GCN)framework,we integrate graph-based embeddings with conventional anomaly detection models,enhancing performance through relational insights.Ourmethod is validated on European credit card transaction data,demonstrating its effectiveness in detecting fraudulent transactions,particularly thosewith subtle patterns that evade traditional,amountbased detection techniques.The results highlight the advantages of incorporating temporal and structural dependencies into fraud detection,showcasing the robustness and applicability of our approach in complex,real-world scenarios.展开更多
Most Convolutional Neural Network(CNN)interpretation techniques visualize only the dominant cues that the model relies on,but there is no guarantee that these represent all the evidence the model uses for classificati...Most Convolutional Neural Network(CNN)interpretation techniques visualize only the dominant cues that the model relies on,but there is no guarantee that these represent all the evidence the model uses for classification.This limitation becomes critical when hidden secondary cues—potentially more meaningful than the visualized ones—remain undiscovered.This study introduces CasCAM(Cascaded Class Activation Mapping)to address this fundamental limitation through counterfactual reasoning.By asking“if this dominant cue were absent,what other evidence would the model use?”,CasCAM progressively masks the most salient features and systematically uncovers the hierarchy of classification evidence hidden beneath them.Experimental results demonstrate that CasCAM effectively discovers the full spectrum of reasoning evidence and can be universally applied with nine existing interpretation methods.展开更多
Groundwater modeling remains challenging due to heterogeneity and complexity of aquifer systems,necessitating endeavors to quantify Groundwater Levels(GWL)dynamics to inform policymakers and hydrogeologists.This study...Groundwater modeling remains challenging due to heterogeneity and complexity of aquifer systems,necessitating endeavors to quantify Groundwater Levels(GWL)dynamics to inform policymakers and hydrogeologists.This study introduces a novel Fuzzy Nonlinear Additive Regression(FNAR)model to predict monthly GWL in an unconfined aquifer in eastern Iran,using a 19-year(1998–2017)dataset from 11 piezometric wells.Under three distinct scenarios with progressively increasing input complexity,the study utilized readily available climate data,including Precipitation(Prc),Temperature(Tave),Relative Humidity(RH),and Evapotranspiration(ETo).The dataset was split into training(70%)and validation(30%)subsets.Results showed that among three input scenarios,Scenario 3(Sc3,incorporating all four variables)achieved the best predictive performance,with RMSE ranging from 0.305 m to 0.768 m,MAE from 0.203 m to 0.522 m,NSE from 0.661 to 0.980,and PBIAS from 0.771%to 0.981%,indicating low bias and high reliability.However,Sc2(excluding ETo)with RMSE ranging from 0.4226 m to 0.9909 m,MAE from 0.3418 m to 0.8173 m,NSE from 0.2831 to 0.9674,and PBIAS from−0.598%to 0.968%across different months offers practical advantages in data-scarce settings.The FNAR model outperforms conventional Fuzzy Least Squares Regression(FLSR)and holds promise for GWL forecasting in data-scarce regions where physical or numerical models are impractical.Future research should focus on integrating FNAR with deep learning algorithms and real-time data assimilation expanding applications across diverse hydrogeological settings.展开更多
As a large family of RNA helicases,DEAD-box(DDX)RNA helicases play crucial roles in almost all cellular RNA processing activities.However,the role of the DDX gene family in cold tolerance of mei(Prunus mume)remains un...As a large family of RNA helicases,DEAD-box(DDX)RNA helicases play crucial roles in almost all cellular RNA processing activities.However,the role of the DDX gene family in cold tolerance of mei(Prunus mume)remains unclear.In this study,we identified 45 DDX genes through whole-genome analysis unevenly distributed across eight chromosomes and scaffolds of mei.Based on the phylogenetic tree and gene structure analysis,the DDX genes were classified into nine subfamilies based on their motif compositions and intron-exon structures.The results of synteny analysis showed that segmental duplication was considered a major factor contributing to the amplification of the PmDDX family.RNA-Seq and qRT-PCR results revealed differential expression of PmDDX genes under cold stress.Among these,PmDDX39 was significantly up-regulated under cold stress,suggesting its positive role in modulating mei cold tolerance.We found that silenced PmDDX39 under cold stress led to greater damage than the wild seedlings via virus-induced gene silencing(VIGS).Conversely,overexpression of PmDDX39 in Arabidopsis enhanced cold stress tolerance.Moreover,dual luciferase and yeast one-hybrid(Y1H)demonstrated that PmDDX39 directly activates the expression of the C-repeat binding factor(PmCBFf)by binding to its promoters.This study provides new insights into the structure,evolution,and functional role of the PmDDX gene family in mei responses to cold stress.展开更多
Background:Os Draconis is an important material in traditional Chinese medicine(TCM).However,its market is saturated with counterfeit products,and the limitations of current identification methods pose a serious threa...Background:Os Draconis is an important material in traditional Chinese medicine(TCM).However,its market is saturated with counterfeit products,and the limitations of current identification methods pose a serious threat to clinical effectiveness and drug safety.This study aims to establish a more accurate and comprehensive authentication system for Os Draconis.Methods:A comprehensive approach was employed to analyze authentic Os Draconis,fossilized Os Draconis,counterfeit products,and lab-prepared modern animal bones.The analytical techniques included ^(14)C dating,electron probe microanalysis(EPMA),polarized light microscopy,X-ray diffraction(XRD),inductively coupled plasma mass spectrometry(ICP-MS),and fourier-transform infrared spectroscopy(FTIR).The study focused on examining the microstructural features and micro-area elemental compositions to identify distinguishing characteristics.Results:Physical identification alone was insufficient to reliably distinguish authentic Os Draconis from its counterfeits.XRD analysis revealed that while hydroxyapatite is the main component in all samples,authentic Os Draconis also contains calcium carbonate and quartz,which were absent in counterfeit and lab-prepared samples.FTIR spectra identified the carbonate ion(CO_(3)^(2-))as a characteristic infrared marker for authentic Os Draconis.ICP-MS analysis showed that Ca and P are the major elements,with a notably high content of Lanthanum(La)among rare earth elements in authentic samples.The EPMA results demonstrated that the Ca/P ratio of authentic Os Draconis is distinct,falling between that of fossilized Os Draconis and counterfeit samples.Conclusion:This study successfully identified several precise markers,including the presence of calcium carbonate,the characteristic CO_(3)^(2-)infrared peak,a high La content,and a specific Ca/P ratio,for the accurate and rapid authentication of Os Draconis.Furthermore,the analysis of its natural porous structure,suitable pore size,and surface area suggests that Os Draconis has significant potential as a natural drug carrier.展开更多
AIM:To evaluate long-term visual field(VF)prediction using K-means clustering in patients with primary open angle glaucoma(POAG).METHODS:Patients who underwent 24-2 VF tests≥10 were included in this study.Using 52 to...AIM:To evaluate long-term visual field(VF)prediction using K-means clustering in patients with primary open angle glaucoma(POAG).METHODS:Patients who underwent 24-2 VF tests≥10 were included in this study.Using 52 total deviation values(TDVs)from the first 10 VF tests of the training dataset,VF points were clustered into several regions using the hierarchical ordered partitioning and collapsing hybrid(HOPACH)and K-means clustering.Based on the clustering results,a linear regression analysis was applied to each clustered region of the testing dataset to predict the TDVs of the 10th VF test.Three to nine VF tests were used to predict the 10th VF test,and the prediction errors(root mean square error,RMSE)of each clustering method and pointwise linear regression(PLR)were compared.RESULTS:The training group consisted of 228 patients(mean age,54.20±14.38y;123 males and 105 females),and the testing group included 81 patients(mean age,54.88±15.22y;43 males and 38 females).All subjects were diagnosed with POAG.Fifty-two VF points were clustered into 11 and nine regions using HOPACH and K-means clustering,respectively.K-means clustering had a lower prediction error than PLR when n=1:3 and 1:4(both P≤0.003).The prediction errors of K-means clustering were lower than those of HOPACH in all sections(n=1:4 to 1:9;all P≤0.011),except for n=1:3(P=0.680).PLR outperformed K-means clustering only when n=1:8 and 1:9(both P≤0.020).CONCLUSION:K-means clustering can predict longterm VF test results more accurately in patients with POAG with limited VF data.展开更多
AIM:To build a functional generalized estimating equation(GEE)model to detect glaucomatous visual field progression and compare the performance of the proposed method with that of commonly employed algorithms.METHODS:...AIM:To build a functional generalized estimating equation(GEE)model to detect glaucomatous visual field progression and compare the performance of the proposed method with that of commonly employed algorithms.METHODS:Totally 716 eyes of 716 patients with primary open angle glaucoma(POAG)with at least 5 reliable 24-2 test results and 2y of follow-up were selected.The functional GEE model was used to detect perimetric progression in the training dataset(501 eyes).In the testing dataset(215 eyes),progression was evaluated the functional GEE model,mean deviation(MD)and visual field index(VFI)rates of change,Advanced Glaucoma Intervention Study(AGIS)and Collaborative Initial Glaucoma Treatment Study(CIGTS)scores,and pointwise linear regression(PLR).RESULTS:The proposed method showed the highest proportion of eyes detected as progression(54.4%),followed by the VFI rate(34.4%),PLR(23.3%),and MD rate(21.4%).The CIGTS and AGIS scores had a lower proportion of eyes detected as progression(7.9%and 5.1%,respectively).The time to detection of progression was significantly shorter for the proposed method than that of other algorithms(adjusted P≤0.019).The VFI rate displayed moderate pairwise agreement with the proposed method(k=0.47).CONCLUSION:The functional GEE model shows the highest proportion of eyes detected as perimetric progression and the shortest time to detect perimetric progression in patients with POAG.展开更多
Remote sensing plays a pivotal role in forest inventory by enabling efficient large-scale monitoring while minimizing fieldwork costs.However,missing values pose a critical challenge in remote sensing applications,as ...Remote sensing plays a pivotal role in forest inventory by enabling efficient large-scale monitoring while minimizing fieldwork costs.However,missing values pose a critical challenge in remote sensing applications,as ignoring or mishandling such data gaps can introduce systematic bias into the estimation of target variables for natural resource monitoring.This can lead to cascading errors that propagate through forest and ecosystem management decisions,ultimately hindering progress toward sustainable forest management,biodiversity conservation,and climate change mitigation strategies.This study aims to propose and demonstrate a procedure that employs hybrid estimators to address the limitations of missing remotely sensed data in forest inventory,using Landsat 7 ETM+SLC-off data as an archived source for forest resource monitoring as a case in point.We compared forest inventory estimates from the hybrid estimator with those from a conventional model-based(CMB)estimator using Sentinel-2 data without missing values.Monte Carlo simulations revealed three key findings:(1)The hybrid estimator,leveraging missing-data remote sensing represented by Landsat 7 ETM+SLCoff data,achieved a sampling precision of over 90%,meeting China's national standard for the National Forest Inventory(NFI);(2)The hybrid estimator demonstrated comparable efficiency to the CMB estimator;(3)The uncertainty associated with hybrid estimators was primarily dominated by model parameter estimation,which could be effectively mitigated by slightly increasing the training sample size or refining model specification.Overall,in forest inventory,the hybrid estimator can surmount the limitations posed by missing values in remotely sensed auxiliary data,effectively balancing cost-effectiveness and flexibility.展开更多
Moments of generalized order statistics appear in several areas of science and engineering.These moments are useful in studying properties of the random variables which are arranged in increasing order of importance,f...Moments of generalized order statistics appear in several areas of science and engineering.These moments are useful in studying properties of the random variables which are arranged in increasing order of importance,for example,time to failure of a computer system.The computation of these moments is sometimes very tedious and hence some algorithms are required.One algorithm is to use a recursive method of computation of these moments and is very useful as it provides the basis to compute higher moments of generalized order statistics from the corresponding lower-order moments.Generalized order statistics pro-vides several models of ordered data as a special case.The moments of general-ized order statistics also provide moments of order statistics and record values as a special case.In this research,the recurrence relations for single,product,inverse and ratio moments of generalized order statistics will be obtained for Lindley–Weibull distribution.These relations will be helpful for obtained moments of gen-eralized order statistics from Lindley–Weibull distribution recursively.Special cases of the recurrence relations will also be obtained.Some characterizations of the distribution will also be obtained by using moments of generalized order statistics.These relations for moments and characterizations can be used in differ-ent areas of computer sciences where data is arranged in increasing order.展开更多
We study a well-known problem concerning a random variable uniformly distributed between two independent random variables. Two different extensions, randomly weighted average on independent random variables and random...We study a well-known problem concerning a random variable uniformly distributed between two independent random variables. Two different extensions, randomly weighted average on independent random variables and randomly weighted average on order statistics, have been introduced for this problem. For the second method, two-sided power random variables have been defined. By using classic method and power technical method, we study some properties for these random variables.展开更多
In this paper explicit expressions and some recurrence relations are derived for marginal and joint moment generating functions of generalized order statistics from Erlang-truncated exponential distribution. The resul...In this paper explicit expressions and some recurrence relations are derived for marginal and joint moment generating functions of generalized order statistics from Erlang-truncated exponential distribution. The results for k-th record values and order statistics are deduced from the relations derived. Further, a characterizing result of this distribution on using the conditional expectation of function of generalized order statistics is discussed.展开更多
Access block,known as exit block or boarding,is defined as a situation in which patients who are admitted or planned for admission remain in the emergency department(ED)as they are unable to be transferred to an inpat...Access block,known as exit block or boarding,is defined as a situation in which patients who are admitted or planned for admission remain in the emergency department(ED)as they are unable to be transferred to an inpatient unit within a reasonable time frame(no longer than 8 hours).[1,2]Access block often occurs due to insufficient hospital capacity and is a major issue in emergency medicine.[3]展开更多
For characterization of negative exponential distribution one needs any arbitrary non constant function only in place of approaches such as identical distributions, absolute continuity, constancy of regression of orde...For characterization of negative exponential distribution one needs any arbitrary non constant function only in place of approaches such as identical distributions, absolute continuity, constancy of regression of order statistics, continuity and linear regression of order statistics, non-degeneracy etc. available in the literature. Recently Bhatt characterized negative exponential distribution through expectation of non constant function of random variable. Attempt is made to extend the characterization of negative exponential distribution through expectation of any arbitrary non constant function of order statistics.展开更多
For characterization of Pareto distribution one needs any arbitrary non constant function only by approach of identity of distribution and equality of expectation of function of random variable in place of approaches ...For characterization of Pareto distribution one needs any arbitrary non constant function only by approach of identity of distribution and equality of expectation of function of random variable in place of approaches such as relation （linear） in （economic variation） reported and true income, independency of suitable function of order statistics, mean and the extreme observation of the sample etc. Examples are given for illustrative purpose展开更多
Mixtures of lifetime distributions occur when two different causes of failure arc present, each with the same parametric form of lifetime distributions. This paper is considered with the mixture model of exponentiated...Mixtures of lifetime distributions occur when two different causes of failure arc present, each with the same parametric form of lifetime distributions. This paper is considered with the mixture model of exponentiated Rayleigh and exponentiated exponential distributions. The author＇s objectives are finding the statistical properties of the model and estimating the parameters of the model by using point estimation and interval estimation methods. First, some properties of the model with some graphs of the density function are discussed. Next, the maximum likelihood method of estimation is used for estimating scale and shape parameters of the model. Estimating the parameters is studied under complete and type II censored samples for different sample sizes. Asymptotic Fisher information matrix of the estimators for complete samples is founded with different sample sizes. The asymptotic variances of the maximum likelihood estimates are derived. Based on the asymptotic variances of the maximum likelihood estimates, interval estimates of the parameters are obtained. Some of the equations in this paper are solved by using numerical iteration such as Newton Raphson method by using Mathematica 7.0. The performance of findings in the paper is showed by demonstrating some numerical illustrations through Monte Carlo simulation study based on absolute relative bias and mean square error.展开更多
Financial pressure of multifactorial etiology promises to create new obstacles for academic anesthesia departments. Integrating the priorities of the academic and clinical mission of the anesthesia department, the med...Financial pressure of multifactorial etiology promises to create new obstacles for academic anesthesia departments. Integrating the priorities of the academic and clinical mission of the anesthesia department, the medical school, and the university hospital will require that anesthesia departments operate with maximal operational efficiency. Maintenance or expansion of institutional infrastructural support of the university anesthesia department will be necessary to achieve operational efficiencies, and to ensure that the safety of our patients is in no way compromised by financial concerns. Previous studies have documented increasing need for monetary institutional supports of academic anesthesia departments [1]. The purpose of this study is to delineate non-monetary institutional support afforded to academic anesthesia departments by their University Hospitals. After IRB approval, we electronically solicited the response to a 63 question survey (43 of which were used for the present study) from all 133 chairpersons of academic anesthesia departments in the United States. The remaining 20 questions were unrelated to the topics presented in this manuscript. 62 responded electronically, for an overall response rate of 46.6%. This study establishes the current state of infrastructural support afforded to academic anesthesia departments in the United States.展开更多
Background: Suicidal attempt in children is a serious public health problem. A proper identification of features of suicide-related behavior may help physicians to develop an accurate approach. The aim of this study w...Background: Suicidal attempt in children is a serious public health problem. A proper identification of features of suicide-related behavior may help physicians to develop an accurate approach. The aim of this study was to clarify the characteristics of children with poisoning due to suicidal attempt and to determine the risk factors of suicidal re-attempt in the Emergency Department (ED) via a simple questionnaire. Methods: We collected medical data of patients under 18 years who were admitted to our ED with intoxication due to suicidal attempt, retrospectively. General characteristics of patients were evaluated. Patients were divided into 2 groups as 1) High risk: patients with repetitive suicide attempt;2) Low risk: patients with first time suicidal attempt. Results: A total of 57 patients were included in this study. The mean age was 15.91 ± 0.97. Majority of the patients were female (73.7%). Analgesics were the most frequent abused drugs with a ratio of 51.1%. It is determined that the most important variables affecting the risk of suicidal re-attempt are “idea about the suicide” and “purpose”. It was determined that patients with an idea of repetitive suicide (I will try again) and whose purpose was to die (I wish I have died) were in the most risky group with a history of previous suicidal attempt. Conclusion: This study suggests that answers of the pediatric patients to some question have a potential to predict the high risk patients. The risk of suicidal re-attempt may be predicted by the answers given to these questions: 1) What is your idea about suicide? 2) What was your purpose?展开更多
Rock classification plays a crucial role in various fields such as geology,engineering,and environmental studies.Employing deep learning AI(artificial intelligence)methods has a high potential to significantly improve...Rock classification plays a crucial role in various fields such as geology,engineering,and environmental studies.Employing deep learning AI(artificial intelligence)methods has a high potential to significantly improve the accuracy and efficiency of this task.The paper delves into the exploration of two cuttingedge AI techniques,namely Mask DINO and Mask R-CNN(convolutional neural network),as means to identify rock weathering grades and rock types.The results demonstrate that Mask DINO,which is a Detection Transformer(DETR),outperforms Mask R-CNN for the aforementioned purposes.Mask DINO achieved f-1 scores of 91% and 86% in weathering grade detection and rock type detection,as opposed to the Mask R-CNN's f-1 scores of 84% and 75%,respectively.These findings underscore the substantial potential of employing DETR algorithms like Mask DINO for automatic classification of both rock type and weathering states.Although the study examines only two AI models,the data processing and other techniques developed in this study may serve as a foundation for future advancements in the field.By incorporating these advanced AI techniques,logging personnel can obtain valuable references to aid their work,ultimately contributing to the advancement of geological and related fields.展开更多
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
基金supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(RS-2023-00249743).
文摘This study proposes a novel forecasting framework that simultaneously captures the strong periodicity and irregular meteorological fluctuations inherent in solar radiation time series.Existing approaches typically define inter-regional correlations using either simple correlation coefficients or distance-based measures when applying spatio-temporal graph neural networks(STGNNs).However,such definitions are prone to generating spurious correlations due to the dominance of periodic structures.To address this limitation,we adopt the Elastic-Band Transform(EBT)to decompose solar radiation into periodic and amplitude-modulated components,which are then modeled independently with separate graph neural networks.The periodic component,characterized by strong nationwide correlations,is learned with a relatively simple architecture,whereas the amplitude-modulated component is modeled with more complex STGNNs that capture climatological similarities between regions.The predictions from the two components are subsequently recombined to yield final forecasts that integrate both periodic patterns and aperiodic variability.The proposed framework is validated with multiple STGNN architectures,and experimental results demonstrate improved predictive accuracy and interpretability compared to conventional methods.
基金supported by the National Research Foundation of Korea(NRF)funded by the Korea government(RS-2023-00249743)Additionally,this research was supported by the Global-Learning&Academic Research Institution for Master’s,PhD Students,and Postdocs(LAMP)Program of the National Research Foundation of Korea(NRF)grant funded by the Ministry of Education(RS-2024-00443714)This research was also supported by the“Research Base Construction Fund Support Program”funded by Jeonbuk National University in 2025.
文摘Traditional anomaly detection methods often assume that data points are independent or exhibit regularly structured relationships,as in Euclidean data such as time series or image grids.However,real-world data frequently involve irregular,interconnected structures,requiring a shift toward non-Euclidean approaches.This study introduces a novel anomaly detection framework designed to handle non-Euclidean data by modeling transactions as graph signals.By leveraging graph convolution filters,we extract meaningful connection strengths that capture relational dependencies often overlooked in traditional methods.Utilizing the Graph Convolutional Networks(GCN)framework,we integrate graph-based embeddings with conventional anomaly detection models,enhancing performance through relational insights.Ourmethod is validated on European credit card transaction data,demonstrating its effectiveness in detecting fraudulent transactions,particularly thosewith subtle patterns that evade traditional,amountbased detection techniques.The results highlight the advantages of incorporating temporal and structural dependencies into fraud detection,showcasing the robustness and applicability of our approach in complex,real-world scenarios.
基金supported by the Basic Science Research Program through the National Research Foundation of Korea(NRF),funded by the Ministry of Education(RS-2023-00249743).
文摘Most Convolutional Neural Network(CNN)interpretation techniques visualize only the dominant cues that the model relies on,but there is no guarantee that these represent all the evidence the model uses for classification.This limitation becomes critical when hidden secondary cues—potentially more meaningful than the visualized ones—remain undiscovered.This study introduces CasCAM(Cascaded Class Activation Mapping)to address this fundamental limitation through counterfactual reasoning.By asking“if this dominant cue were absent,what other evidence would the model use?”,CasCAM progressively masks the most salient features and systematically uncovers the hierarchy of classification evidence hidden beneath them.Experimental results demonstrate that CasCAM effectively discovers the full spectrum of reasoning evidence and can be universally applied with nine existing interpretation methods.
基金supported by the Iran National Science Foundation(INSF)the University of Birjand under grant number 4034771.
文摘Groundwater modeling remains challenging due to heterogeneity and complexity of aquifer systems,necessitating endeavors to quantify Groundwater Levels(GWL)dynamics to inform policymakers and hydrogeologists.This study introduces a novel Fuzzy Nonlinear Additive Regression(FNAR)model to predict monthly GWL in an unconfined aquifer in eastern Iran,using a 19-year(1998–2017)dataset from 11 piezometric wells.Under three distinct scenarios with progressively increasing input complexity,the study utilized readily available climate data,including Precipitation(Prc),Temperature(Tave),Relative Humidity(RH),and Evapotranspiration(ETo).The dataset was split into training(70%)and validation(30%)subsets.Results showed that among three input scenarios,Scenario 3(Sc3,incorporating all four variables)achieved the best predictive performance,with RMSE ranging from 0.305 m to 0.768 m,MAE from 0.203 m to 0.522 m,NSE from 0.661 to 0.980,and PBIAS from 0.771%to 0.981%,indicating low bias and high reliability.However,Sc2(excluding ETo)with RMSE ranging from 0.4226 m to 0.9909 m,MAE from 0.3418 m to 0.8173 m,NSE from 0.2831 to 0.9674,and PBIAS from−0.598%to 0.968%across different months offers practical advantages in data-scarce settings.The FNAR model outperforms conventional Fuzzy Least Squares Regression(FLSR)and holds promise for GWL forecasting in data-scarce regions where physical or numerical models are impractical.Future research should focus on integrating FNAR with deep learning algorithms and real-time data assimilation expanding applications across diverse hydrogeological settings.
基金supported by the Fundamental Research Funds for the Central Universities(Grant No.QNTD202503)Forestry and Grassland Science and Technology Innovation Youth Top Talent Project of China(Grant No.2020132608)Beijing High-Precision Discipline Project,Discipline of Ecological Environment of Urban and Rural Human Settlements.
文摘As a large family of RNA helicases,DEAD-box(DDX)RNA helicases play crucial roles in almost all cellular RNA processing activities.However,the role of the DDX gene family in cold tolerance of mei(Prunus mume)remains unclear.In this study,we identified 45 DDX genes through whole-genome analysis unevenly distributed across eight chromosomes and scaffolds of mei.Based on the phylogenetic tree and gene structure analysis,the DDX genes were classified into nine subfamilies based on their motif compositions and intron-exon structures.The results of synteny analysis showed that segmental duplication was considered a major factor contributing to the amplification of the PmDDX family.RNA-Seq and qRT-PCR results revealed differential expression of PmDDX genes under cold stress.Among these,PmDDX39 was significantly up-regulated under cold stress,suggesting its positive role in modulating mei cold tolerance.We found that silenced PmDDX39 under cold stress led to greater damage than the wild seedlings via virus-induced gene silencing(VIGS).Conversely,overexpression of PmDDX39 in Arabidopsis enhanced cold stress tolerance.Moreover,dual luciferase and yeast one-hybrid(Y1H)demonstrated that PmDDX39 directly activates the expression of the C-repeat binding factor(PmCBFf)by binding to its promoters.This study provides new insights into the structure,evolution,and functional role of the PmDDX gene family in mei responses to cold stress.
基金supported by the Scientific and Technological Innovation Project of the China Academy of Chinese Medical Sciences(CI2021A04013)the National Natural Science Foundation of China(82204610)+1 种基金the Qihang Talent Program(L2022046)the Fundamental Research Funds for the Central Public Welfare Research Institutes(ZZ15-YQ-041 and L2021029).
文摘Background:Os Draconis is an important material in traditional Chinese medicine(TCM).However,its market is saturated with counterfeit products,and the limitations of current identification methods pose a serious threat to clinical effectiveness and drug safety.This study aims to establish a more accurate and comprehensive authentication system for Os Draconis.Methods:A comprehensive approach was employed to analyze authentic Os Draconis,fossilized Os Draconis,counterfeit products,and lab-prepared modern animal bones.The analytical techniques included ^(14)C dating,electron probe microanalysis(EPMA),polarized light microscopy,X-ray diffraction(XRD),inductively coupled plasma mass spectrometry(ICP-MS),and fourier-transform infrared spectroscopy(FTIR).The study focused on examining the microstructural features and micro-area elemental compositions to identify distinguishing characteristics.Results:Physical identification alone was insufficient to reliably distinguish authentic Os Draconis from its counterfeits.XRD analysis revealed that while hydroxyapatite is the main component in all samples,authentic Os Draconis also contains calcium carbonate and quartz,which were absent in counterfeit and lab-prepared samples.FTIR spectra identified the carbonate ion(CO_(3)^(2-))as a characteristic infrared marker for authentic Os Draconis.ICP-MS analysis showed that Ca and P are the major elements,with a notably high content of Lanthanum(La)among rare earth elements in authentic samples.The EPMA results demonstrated that the Ca/P ratio of authentic Os Draconis is distinct,falling between that of fossilized Os Draconis and counterfeit samples.Conclusion:This study successfully identified several precise markers,including the presence of calcium carbonate,the characteristic CO_(3)^(2-)infrared peak,a high La content,and a specific Ca/P ratio,for the accurate and rapid authentication of Os Draconis.Furthermore,the analysis of its natural porous structure,suitable pore size,and surface area suggests that Os Draconis has significant potential as a natural drug carrier.
基金Supported by the Korea Health Technology R&D Project through the Korea Health Industry Development Institute(KHIDI),the Ministry of Health&Welfare,Republic of Korea(No.RS-2020-KH088726)the Patient-Centered Clinical Research Coordinating Center(PACEN),the Ministry of Health and Welfare,Republic of Korea(No.HC19C0276)the National Research Foundation of Korea(NRF),the Korea Government(MSIT)(No.RS-2023-00247504).
文摘AIM:To evaluate long-term visual field(VF)prediction using K-means clustering in patients with primary open angle glaucoma(POAG).METHODS:Patients who underwent 24-2 VF tests≥10 were included in this study.Using 52 total deviation values(TDVs)from the first 10 VF tests of the training dataset,VF points were clustered into several regions using the hierarchical ordered partitioning and collapsing hybrid(HOPACH)and K-means clustering.Based on the clustering results,a linear regression analysis was applied to each clustered region of the testing dataset to predict the TDVs of the 10th VF test.Three to nine VF tests were used to predict the 10th VF test,and the prediction errors(root mean square error,RMSE)of each clustering method and pointwise linear regression(PLR)were compared.RESULTS:The training group consisted of 228 patients(mean age,54.20±14.38y;123 males and 105 females),and the testing group included 81 patients(mean age,54.88±15.22y;43 males and 38 females).All subjects were diagnosed with POAG.Fifty-two VF points were clustered into 11 and nine regions using HOPACH and K-means clustering,respectively.K-means clustering had a lower prediction error than PLR when n=1:3 and 1:4(both P≤0.003).The prediction errors of K-means clustering were lower than those of HOPACH in all sections(n=1:4 to 1:9;all P≤0.011),except for n=1:3(P=0.680).PLR outperformed K-means clustering only when n=1:8 and 1:9(both P≤0.020).CONCLUSION:K-means clustering can predict longterm VF test results more accurately in patients with POAG with limited VF data.
基金Supported by the Korea Health Technology R&D Project through the Korea Health Industry Development Institute(KHIDI),funded by the Ministry of Health&Welfare,Republic of Korea(No.HR20C0026)the National Research Foundation of Korea(NRF)(No.RS-2023-00247504)the Patient-Centered Clinical Research Coordinating Center,funded by the Ministry of Health&Welfare,Republic of Korea(No.HC19C0276).
文摘AIM:To build a functional generalized estimating equation(GEE)model to detect glaucomatous visual field progression and compare the performance of the proposed method with that of commonly employed algorithms.METHODS:Totally 716 eyes of 716 patients with primary open angle glaucoma(POAG)with at least 5 reliable 24-2 test results and 2y of follow-up were selected.The functional GEE model was used to detect perimetric progression in the training dataset(501 eyes).In the testing dataset(215 eyes),progression was evaluated the functional GEE model,mean deviation(MD)and visual field index(VFI)rates of change,Advanced Glaucoma Intervention Study(AGIS)and Collaborative Initial Glaucoma Treatment Study(CIGTS)scores,and pointwise linear regression(PLR).RESULTS:The proposed method showed the highest proportion of eyes detected as progression(54.4%),followed by the VFI rate(34.4%),PLR(23.3%),and MD rate(21.4%).The CIGTS and AGIS scores had a lower proportion of eyes detected as progression(7.9%and 5.1%,respectively).The time to detection of progression was significantly shorter for the proposed method than that of other algorithms(adjusted P≤0.019).The VFI rate displayed moderate pairwise agreement with the proposed method(k=0.47).CONCLUSION:The functional GEE model shows the highest proportion of eyes detected as perimetric progression and the shortest time to detect perimetric progression in patients with POAG.
基金supported by the National Key R&D Program of China(No.2023YFF1304002-05)the National Social Science Fund of China(No.22BTJ005)the National Natural Science Foundation of China(No.32572049)。
文摘Remote sensing plays a pivotal role in forest inventory by enabling efficient large-scale monitoring while minimizing fieldwork costs.However,missing values pose a critical challenge in remote sensing applications,as ignoring or mishandling such data gaps can introduce systematic bias into the estimation of target variables for natural resource monitoring.This can lead to cascading errors that propagate through forest and ecosystem management decisions,ultimately hindering progress toward sustainable forest management,biodiversity conservation,and climate change mitigation strategies.This study aims to propose and demonstrate a procedure that employs hybrid estimators to address the limitations of missing remotely sensed data in forest inventory,using Landsat 7 ETM+SLC-off data as an archived source for forest resource monitoring as a case in point.We compared forest inventory estimates from the hybrid estimator with those from a conventional model-based(CMB)estimator using Sentinel-2 data without missing values.Monte Carlo simulations revealed three key findings:(1)The hybrid estimator,leveraging missing-data remote sensing represented by Landsat 7 ETM+SLCoff data,achieved a sampling precision of over 90%,meeting China's national standard for the National Forest Inventory(NFI);(2)The hybrid estimator demonstrated comparable efficiency to the CMB estimator;(3)The uncertainty associated with hybrid estimators was primarily dominated by model parameter estimation,which could be effectively mitigated by slightly increasing the training sample size or refining model specification.Overall,in forest inventory,the hybrid estimator can surmount the limitations posed by missing values in remotely sensed auxiliary data,effectively balancing cost-effectiveness and flexibility.
基金The work was funded by the University of Jeddah,Saudi Arabia under Grant Number UJ–02–093–DR.The authors,therefore,acknowledge with thanks the University for technical and financial support.
文摘Moments of generalized order statistics appear in several areas of science and engineering.These moments are useful in studying properties of the random variables which are arranged in increasing order of importance,for example,time to failure of a computer system.The computation of these moments is sometimes very tedious and hence some algorithms are required.One algorithm is to use a recursive method of computation of these moments and is very useful as it provides the basis to compute higher moments of generalized order statistics from the corresponding lower-order moments.Generalized order statistics pro-vides several models of ordered data as a special case.The moments of general-ized order statistics also provide moments of order statistics and record values as a special case.In this research,the recurrence relations for single,product,inverse and ratio moments of generalized order statistics will be obtained for Lindley–Weibull distribution.These relations will be helpful for obtained moments of gen-eralized order statistics from Lindley–Weibull distribution recursively.Special cases of the recurrence relations will also be obtained.Some characterizations of the distribution will also be obtained by using moments of generalized order statistics.These relations for moments and characterizations can be used in differ-ent areas of computer sciences where data is arranged in increasing order.
文摘We study a well-known problem concerning a random variable uniformly distributed between two independent random variables. Two different extensions, randomly weighted average on independent random variables and randomly weighted average on order statistics, have been introduced for this problem. For the second method, two-sided power random variables have been defined. By using classic method and power technical method, we study some properties for these random variables.
文摘In this paper explicit expressions and some recurrence relations are derived for marginal and joint moment generating functions of generalized order statistics from Erlang-truncated exponential distribution. The results for k-th record values and order statistics are deduced from the relations derived. Further, a characterizing result of this distribution on using the conditional expectation of function of generalized order statistics is discussed.
基金supported by the Project of Science and Technology Commission of Jiading,Shanghai(JDKW-2016-W03)the Scientific Research Projects of the Shanghai Municipal Health Commission for Youths(20204Y0016)+1 种基金the National Natural Science Foundation of China(72174041)。
文摘Access block,known as exit block or boarding,is defined as a situation in which patients who are admitted or planned for admission remain in the emergency department(ED)as they are unable to be transferred to an inpatient unit within a reasonable time frame(no longer than 8 hours).[1,2]Access block often occurs due to insufficient hospital capacity and is a major issue in emergency medicine.[3]
文摘For characterization of negative exponential distribution one needs any arbitrary non constant function only in place of approaches such as identical distributions, absolute continuity, constancy of regression of order statistics, continuity and linear regression of order statistics, non-degeneracy etc. available in the literature. Recently Bhatt characterized negative exponential distribution through expectation of non constant function of random variable. Attempt is made to extend the characterization of negative exponential distribution through expectation of any arbitrary non constant function of order statistics.
文摘For characterization of Pareto distribution one needs any arbitrary non constant function only by approach of identity of distribution and equality of expectation of function of random variable in place of approaches such as relation （linear） in （economic variation） reported and true income, independency of suitable function of order statistics, mean and the extreme observation of the sample etc. Examples are given for illustrative purpose
文摘Mixtures of lifetime distributions occur when two different causes of failure arc present, each with the same parametric form of lifetime distributions. This paper is considered with the mixture model of exponentiated Rayleigh and exponentiated exponential distributions. The author＇s objectives are finding the statistical properties of the model and estimating the parameters of the model by using point estimation and interval estimation methods. First, some properties of the model with some graphs of the density function are discussed. Next, the maximum likelihood method of estimation is used for estimating scale and shape parameters of the model. Estimating the parameters is studied under complete and type II censored samples for different sample sizes. Asymptotic Fisher information matrix of the estimators for complete samples is founded with different sample sizes. The asymptotic variances of the maximum likelihood estimates are derived. Based on the asymptotic variances of the maximum likelihood estimates, interval estimates of the parameters are obtained. Some of the equations in this paper are solved by using numerical iteration such as Newton Raphson method by using Mathematica 7.0. The performance of findings in the paper is showed by demonstrating some numerical illustrations through Monte Carlo simulation study based on absolute relative bias and mean square error.
文摘Financial pressure of multifactorial etiology promises to create new obstacles for academic anesthesia departments. Integrating the priorities of the academic and clinical mission of the anesthesia department, the medical school, and the university hospital will require that anesthesia departments operate with maximal operational efficiency. Maintenance or expansion of institutional infrastructural support of the university anesthesia department will be necessary to achieve operational efficiencies, and to ensure that the safety of our patients is in no way compromised by financial concerns. Previous studies have documented increasing need for monetary institutional supports of academic anesthesia departments [1]. The purpose of this study is to delineate non-monetary institutional support afforded to academic anesthesia departments by their University Hospitals. After IRB approval, we electronically solicited the response to a 63 question survey (43 of which were used for the present study) from all 133 chairpersons of academic anesthesia departments in the United States. The remaining 20 questions were unrelated to the topics presented in this manuscript. 62 responded electronically, for an overall response rate of 46.6%. This study establishes the current state of infrastructural support afforded to academic anesthesia departments in the United States.
文摘Background: Suicidal attempt in children is a serious public health problem. A proper identification of features of suicide-related behavior may help physicians to develop an accurate approach. The aim of this study was to clarify the characteristics of children with poisoning due to suicidal attempt and to determine the risk factors of suicidal re-attempt in the Emergency Department (ED) via a simple questionnaire. Methods: We collected medical data of patients under 18 years who were admitted to our ED with intoxication due to suicidal attempt, retrospectively. General characteristics of patients were evaluated. Patients were divided into 2 groups as 1) High risk: patients with repetitive suicide attempt;2) Low risk: patients with first time suicidal attempt. Results: A total of 57 patients were included in this study. The mean age was 15.91 ± 0.97. Majority of the patients were female (73.7%). Analgesics were the most frequent abused drugs with a ratio of 51.1%. It is determined that the most important variables affecting the risk of suicidal re-attempt are “idea about the suicide” and “purpose”. It was determined that patients with an idea of repetitive suicide (I will try again) and whose purpose was to die (I wish I have died) were in the most risky group with a history of previous suicidal attempt. Conclusion: This study suggests that answers of the pediatric patients to some question have a potential to predict the high risk patients. The risk of suicidal re-attempt may be predicted by the answers given to these questions: 1) What is your idea about suicide? 2) What was your purpose?
基金supported by the Construction Industry Council(Grant No.CICR/01/22)the support from the General Research Fund(Grant No.17206822)of the Research Grants Council(Hong Kong).
文摘Rock classification plays a crucial role in various fields such as geology,engineering,and environmental studies.Employing deep learning AI(artificial intelligence)methods has a high potential to significantly improve the accuracy and efficiency of this task.The paper delves into the exploration of two cuttingedge AI techniques,namely Mask DINO and Mask R-CNN(convolutional neural network),as means to identify rock weathering grades and rock types.The results demonstrate that Mask DINO,which is a Detection Transformer(DETR),outperforms Mask R-CNN for the aforementioned purposes.Mask DINO achieved f-1 scores of 91% and 86% in weathering grade detection and rock type detection,as opposed to the Mask R-CNN's f-1 scores of 84% and 75%,respectively.These findings underscore the substantial potential of employing DETR algorithms like Mask DINO for automatic classification of both rock type and weathering states.Although the study examines only two AI models,the data processing and other techniques developed in this study may serve as a foundation for future advancements in the field.By incorporating these advanced AI techniques,logging personnel can obtain valuable references to aid their work,ultimately contributing to the advancement of geological and related fields.
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.
