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
This study aims to examine the explicit solution for calculating the Average Run Length(ARL)on the triple exponentially weighted moving average(TEWMA)control chart applied to autoregressive model(AR(p)),where AR(p)is ...This study aims to examine the explicit solution for calculating the Average Run Length(ARL)on the triple exponentially weighted moving average(TEWMA)control chart applied to autoregressive model(AR(p)),where AR(p)is an autoregressive model of order p,representing a time series with dependencies on its p previous values.Additionally,the study evaluates the accuracy of both explicit and numerical integral equation(NIE)solutions for AR(p)using the TEWMA control chart,focusing on the absolute percentage relative error.The results indicate that the explicit and approximate solutions are in close agreement.Furthermore,the study investigates the performance of exponentially weighted moving average(EWMA)and TEWMA control charts in detecting changes in the process,using the relative mean index(RMI)as a measure.The findings demonstrate that the TEWMA control chart outperforms the EWMA control chart in detecting process changes,especially when the value ofλis sufficiently large.In addition,an analysis using historical data from the SET index between January 2024 and May 2024 and historical data of global annual plastic production,the results of both data sets also emphasize the superior performance of the TEWMA control chart.展开更多
Stem volume estimation is crucial in forest ecology and management,particularly for timber harvesting strategies and carbon stock assessments.This study aimed to develop a variable-exponent taper equation specifically...Stem volume estimation is crucial in forest ecology and management,particularly for timber harvesting strategies and carbon stock assessments.This study aimed to develop a variable-exponent taper equation specifically tailored to savanna tree species using close-range photogrammetry(CRP)data and to evaluate its performance against conventional volume equations for stem volume estimation.A dataset of 30 trees across five dominant savanna species was used to fit the taper model,which was validated using a separate dataset of 322 trees from 14 species.The results demonstrated significant improvements in volume estimation accuracy when using the taper equation.At the tree level,the root mean square error(RMSE)decreased by 47%,from 598 to 319 dm^(3),and the mean absolute bias(MAB)by 48%,from 328 to 172 dm3,compared to volume equations.Similarly,at the plot level,RMSE was reduced by 42% and MAB by 40%.The model performed well for species with regular forms.However,species with irregular tapers exhibited higher errors,reflecting the challenges of modeling stem forms of mixed species.The use of CRP proved valuable,providing high-resolution diameter measurements that improved model parameterization.This study underscores the importance of advanced data collection methods for enhancing taper model accuracy and suggests that further species-specific adjustments are needed to improve performance for species with irregular forms.The findings support the broader application of taper equations for improving stem volume estimates in savanna ecosystems,contributing to better forest management and resource monitoring practices.展开更多
This study presents an innovative development of the exponentially weighted moving average(EWMA)control chart,explicitly adapted for the examination of time series data distinguished by seasonal autoregressive moving ...This study presents an innovative development of the exponentially weighted moving average(EWMA)control chart,explicitly adapted for the examination of time series data distinguished by seasonal autoregressive moving average behavior—SARMA(1,1)L under exponential white noise.Unlike previous works that rely on simplified models such as AR(1)or assume independence,this research derives for the first time an exact two-sided Average Run Length(ARL)formula for theModified EWMAchart under SARMA(1,1)L conditions,using a mathematically rigorous Fredholm integral approach.The derived formulas are validated against numerical integral equation(NIE)solutions,showing strong agreement and significantly reduced computational burden.Additionally,a performance comparison index(PCI)is introduced to assess the chart’s detection capability.Results demonstrate that the proposed method exhibits superior sensitivity to mean shifts in autocorrelated environments,outperforming existing approaches.The findings offer a new,efficient framework for real-time quality control in complex seasonal processes,with potential applications in environmental monitoring and intelligent manufacturing systems.展开更多
In the past decade,financial institutions have invested significant efforts in the development of accurate analytical credit scoring models.The evidence suggests that even small improvements in the accuracy of existin...In the past decade,financial institutions have invested significant efforts in the development of accurate analytical credit scoring models.The evidence suggests that even small improvements in the accuracy of existing credit-scoring models may optimize profits while effectively managing risk exposure.Despite continuing efforts,the majority of existing credit scoring models still include some judgment-based assumptions that are sometimes supported by the significant findings of previous studies but are not validated using the institution’s internal data.We argue that current studies related to the development of credit scoring models have largely ignored recent developments in statistical methods for sufficient dimension reduction.To contribute to the field of financial innovation,this study proposes a Dimension Reduction Assisted Credit Scoring(DRA-CS)method via distance covariance-based sufficient dimension reduction(DCOV-SDR)in Majorization-Minimization(MM)algorithm.First,in the presence of a large number of variables,the DRA-CS method results in greater dimension reduction and better prediction accuracy than the other methods used for dimension reduction.Second,when the DRA-CS method is employed with logistic regression,it outperforms existing methods based on different variable selection techniques.This study argues that the DRA-CS method should be used by financial institutions as a financial innovation tool to analyze high-dimensional customer datasets and improve the accuracy of existing credit scoring methods.展开更多
Put options are known to be priced unusually high in the market,which we refer to as the overpriced put puzzle.This study proposes a quantum model(QM)that can explain such high put option prices as fair prices.Startin...Put options are known to be priced unusually high in the market,which we refer to as the overpriced put puzzle.This study proposes a quantum model(QM)that can explain such high put option prices as fair prices.Starting from a stochastic differential equation of stock returns,we convert the Fokker–Planck equation into the Schr鰀inger equation.To model the market force that always draws excess returns back to equilibrium,we specify a diffusion process corresponding to a QM with a delta potential.The results demonstrate that stock returns follow a Laplace distribution and exhibit power law in the tail.We then construct a closed-form solution for European put option pricing,determining that our model better explains the returns of the S&P 500 index and its corresponding put option prices than do geometric Brownian motion-based models.This study has significant implications for investors and risk managers,presenting a model that can potentially improve derivative pricing.Future studies can generalize the model assumptions by introducing asymmetric potential drawing back excess returns to equilibrium.展开更多
This study explores the magnetohydrodynamic(MHD)boundary layer flow of a water-based Boger nanofluid over a stretching sheet,with particular focus on the influences of nanoparticle diameter,nanolayer effects,and therm...This study explores the magnetohydrodynamic(MHD)boundary layer flow of a water-based Boger nanofluid over a stretching sheet,with particular focus on the influences of nanoparticle diameter,nanolayer effects,and thermal radiation.The primary aim is to examine how variations in nanoparticle size and nanolayer thickness affect the hydrothermal behavior of the nanofluid.The model also incorporates the contributions of viscous dissipation and Joule heating within the heat transfer equation.The governing momentum and energy equations are converted into dimensionless partial differential equations(PDEs)using appropriate similarity variables and are numerically solved using the finite element method(FEM)implemented in MATLAB.Extensive validation of this method confirms its reliability and accuracy in numerical solutions.The findings reveal that increasing the diameter of copper nanoparticles significantly enhances the velocity profile,with a more pronounced effect observed at wider inter-particle spacings.A higher solvent volume fraction leads to decreased velocity and temperature distributions,while a greater relaxation time ratio improves velocity and temperature profiles due to the increased elastic response of the fluid.Moreover,enhancements in the magnetic parameter,thermal radiation,and Eckert number lead to an elevation in temperature profiles.Furthermore,higher nanolayer thickness reduces the temperature profile,whereas particle radius yields the opposite outcome.展开更多
The migration of tasks aided by machine learning(ML)predictions IN(DPM)is a system-level design technique that is used to reduce energy by enhancing the overall performance of the processor.In this paper,we address th...The migration of tasks aided by machine learning(ML)predictions IN(DPM)is a system-level design technique that is used to reduce energy by enhancing the overall performance of the processor.In this paper,we address the issue of system-level higher task dissipation during the execution of parallel workloads with common deadlines by introducing a machine learning-based framework that includes task migration using energy-efficient earliest deadline first scheduling(EA-EDF).ML-based EA-EDF enhances the overall throughput and optimizes the energy to avoid delay and performance degradation in a multiprocessor system.The proposed system model allocates processors to the ready task set in such a way that their deadlines are guaranteed.A full task migration policy is also integrated to ensure proper task mapping that ensures inter-process linkage among the arrived tasks with the same deadlines.The execution of a task can halt on one CPU and reschedule the execution on a different processor to avoid delay and ensure meeting the deadline.Our approach shows promising potential for machine-learning-based schedulability analysis enables a comparison between different ML models and shows a promising reduction in energy as compared with other ML-aware task migration techniques for SoC like Multi-Layer Feed-Forward Neural Networks(MLFNN)based on convolutional neural network(CNN),Random Forest(RF)and Deep learning(DL)algorithm.The Simulations are conducted using super pipelined microarchitecture of advanced micro devices(AMD)XScale PXA270 using instruction and data cache per core 32 Kbyte I-cache and 32 Kbyte D-cache on various utilization factors(u_(i))12%,31%and 50%.The proposed approach consumes 5.3%less energy when almost half of the CPU is running and on a lower workload consumes 1.04%less energy.The proposed design accumulatively gives significant improvements by reducing the energy dissipation on three clock rates by 4.41%,on 624 MHz by 5.4%and 5.9%on applications operating on 416 and 312 MHz standard operating frequencies.展开更多
We construct an exchange-traded fund(ETF)based on the CRyptocurrency IndeX(CRIX),which closely maps nonstationary cryptocurrency(CC)dynamics by adapting the weights of its constituents dynamically.Our scenario analysi...We construct an exchange-traded fund(ETF)based on the CRyptocurrency IndeX(CRIX),which closely maps nonstationary cryptocurrency(CC)dynamics by adapting the weights of its constituents dynamically.Our scenario analysis considers the fee schedules of regulated CC exchanges,spreads obtained from order book data,and investment in-&outflows to the ETF are modelled stochastically.The scenario analysis yields valuable insights into the mechanisms,costs,and risks of this innovative financial product:i)although the composition of the CRIX ETF changes frequently(from 5 to 30 constituents),it remains robust in its core,as the weights of Bitcoin(BTC)and Ethereum(ETH)are robust over time;ii)on average,5.2%needs to be rebalanced on the rebalancing dates;iii)trading costs are low compared with traditional assets;iv)the liquidity of the CC sector increases significantly during the analysis period;spreads occur,especially for altcoins and increase with the size of the transactions.However,because BTC and ETH are the most affected by rebalancing,the cost of spreads remains limited.展开更多
Mesenchymal stem cells （MSCs） of nonembryonic origins possess the proliferation and multi-lineage differentiation potentials. It has been established that epigenetic mechanisms could be critical for determining the ...Mesenchymal stem cells （MSCs） of nonembryonic origins possess the proliferation and multi-lineage differentiation potentials. It has been established that epigenetic mechanisms could be critical for determining the fate of stem cells, and MSCs derived from different origins exhibited different expression profiles individually to a certain extent. In this study, ChiP-on-chip was used to generate genome-wide histone H3-Lys9 acetylation and dimethylation profiles at gene promoters in human bone marrow MSCs. We showed that modifications of histone H3-Lys9 at gene promoters correlated well with mRNA expression in human bone marrow MSCs. Functional analysis revealed that many key cellular pathways in human bone marrow MSC self-renewal, such as the canonical signaling pathways, cell cycle pathways and cytokine related pathways may be regulated by H3-Lys9 modifications. These data suggest that gene activation and silencing affected by H3-Lys9 acetylation and dimethylation, respectively, may be essential to the maintenance of human bone marrow MSC self-renewal and multi-potency.展开更多
Differently from the general online social network(OSN),locationbased mobile social network(LMSN),which seamlessly integrates mobile computing and social computing technologies,has unique characteristics of temporal,s...Differently from the general online social network(OSN),locationbased mobile social network(LMSN),which seamlessly integrates mobile computing and social computing technologies,has unique characteristics of temporal,spatial and social correlation.Recommending friends instantly based on current location of users in the real world has become increasingly popular in LMSN.However,the existing friend recommendation methods based on topological structures of a social network or non-topological information such as similar user profiles cannot well address the instant making friends in the real world.In this article,we analyze users' check-in behavior in a real LMSN site named Gowalla.According to this analysis,we present an approach of recommending friends instantly for LMSN users by considering the real-time physical location proximity,offline behavior similarity and friendship network information in the virtual community simultaneously.This approach effectively bridges the gap between the offline behavior of users in the real world and online friendship network information in the virtual community.Finally,we use the real user check-in dataset of Gowalla to verify the effectiveness of our approach.展开更多
We construct the membership functions of the fuzzy objective values of a controllable queueing model,in which cost elements,arrival rate and service rate are all fuzzy numbers. Based on Zadeh's extension principle...We construct the membership functions of the fuzzy objective values of a controllable queueing model,in which cost elements,arrival rate and service rate are all fuzzy numbers. Based on Zadeh's extension principle,a set of parametric nonlinear programs is developed to find the upper and lower bounds of the minimal average total cost per unit time at the possibility level. The membership functions of the minimal average total cost are further constructed using different values of the possibility level. A numerical example is solved successfully to illustrate the validity of the proposed approach. Because the object value is expressed and governed by the membership functions,the optimization problem in a fuzzy environment for the controllable queueing models is represented more accurately and analytical results are more useful for system designers and practitioners.展开更多
This article addresses a stochastic ratio-dependent predator-prey system with Leslie-Gower and Holling type II schemes. Firstly, the existence of the global positive solution is shown by the comparison theorem of stoc...This article addresses a stochastic ratio-dependent predator-prey system with Leslie-Gower and Holling type II schemes. Firstly, the existence of the global positive solution is shown by the comparison theorem of stochastic differential equations. Secondly, in the case of persistence, we prove that there exists a ergodic stationary distribution. Finally, numerical simulations for a hypothetical set of parameter values are presented to illustrate the analytical findings.展开更多
This paper is concerned with a stochastic HBV infection model with logistic growth. First, by constructing suitable stochastic Lyapunov functions, we establish sufficient conditions for the existence of ergodic statio...This paper is concerned with a stochastic HBV infection model with logistic growth. First, by constructing suitable stochastic Lyapunov functions, we establish sufficient conditions for the existence of ergodic stationary distribution of the solution to the HBV infection model. Then we obtain sufficient conditions for extinction of the disease. The stationary distribution shows that the disease can become persistent in vivo.展开更多
Cover crops are the plants which are grown to improve soil fertility, prevent soil erosion, enrichment and protection of soil, and enhance nutrient and water availability, and quality of soil. Cover crops provide seve...Cover crops are the plants which are grown to improve soil fertility, prevent soil erosion, enrichment and protection of soil, and enhance nutrient and water availability, and quality of soil. Cover crops provide several benefits to soils used for agriculture production. Cover crops are helpful in increasing and sustaining microbial biodiversity in soils. We summarized the effect of several cover crops in soil properties such as soil moisture content, soil microbial activities, soil carbon sequestration, nitrate leaching, soil water, and soil health. Selection of cover crops usually depends on the primary benefits which are provided by cover crops. Other factors may also include weather conditions, time of sowing, either legume or non-legume and timing and method of killing of a cover crop. In recent times, cover crops are also used for mitigating climate change, suppressing weeds in crops and increasing exchangeable nutrients such as Mg2+ and K+. Cover crops are also found to be economical in long-term experiment studies. Although some limitations always come with several benefits. Cover crops have some problems including the method of killing, host for pathogens, regeneration, and not immediate benefits of using them. Despite the few limitations, cover crops improve the overall health of the soil and provide a sustainable environment for the main crops.展开更多
Coronavirus disease 2019 outbreak has spread as a pandemic since the end of year 2019.This situation has been causing a lot of problems of human beings such as economic problems,health problems.The forecasting of the ...Coronavirus disease 2019 outbreak has spread as a pandemic since the end of year 2019.This situation has been causing a lot of problems of human beings such as economic problems,health problems.The forecasting of the number of infectious people is required by the authorities of all countries including Southeast Asian countries to make a decision and control the outbreak.This research is to investigate the suitable forecasting model for the number of infectious people in Southeast Asian countries.A comparison of forecasting models between logistic growth curve which is symmetric and Gompertz growth curve which is asymmetric based on the maximumof Coefficient of Determination and theminimumof RootMean Squared Percentage Error is also proposed.The estimation of parameters of the forecasting models is evaluated by the least square method.In addition,spreading of the outbreak is estimated by the derivative of the number of cumulative cases.The findings show that Gompertz growth curve is a suitable forecasting model for Indonesia,Philippines,andMalaysia and logistic growth curve suits the other countries in South Asia.展开更多
We compare the optimal operating cost of the two bicriterion policies, <p,T> and <p,N>, for an M/G/1 queueing system with second optional service, in which the length of the vacation period is randomly con...We compare the optimal operating cost of the two bicriterion policies, <p,T> and <p,N>, for an M/G/1 queueing system with second optional service, in which the length of the vacation period is randomly controlled either by the number of arrivals during the idle period or by a timer. After all the customers are served in the queue exhaustively, the server immediately takes a vacation and may operate <p,T> policy or <p,N> policy. For the two bicriterion policies, the total average cost function per unit time is developed to search the optimal stationary operating policies at a minimum cost. Based upon the optimal cost the explicit forms for joint optimum threshold values of (p,T) and (p,N) are obtained.展开更多
A modified exponentially weighted moving average (EWMA) scheme is one of the quality control charts suchthat this control chart can quickly detect a small shift. The average run length (ARL) is frequently used for the...A modified exponentially weighted moving average (EWMA) scheme is one of the quality control charts suchthat this control chart can quickly detect a small shift. The average run length (ARL) is frequently used for theperformance evaluation on control charts. This paper proposes the explicit formula for evaluating the average runlength on a two-sided modified exponentially weighted moving average chart under the observations of a first-orderautoregressive process, referred to as AR(1) process, with an exponential white noise. The performance comparisonof the explicit formula and the numerical integral technique is carried out using the absolute relative change forchecking the correct formula and the CPU time for testing speed of calculation. The results show that the ARL ofthe explicit formula and the numerical integral equation method are hardly different, but this explicit formula ismuch faster for calculating the ARL and offered accurate values. Furthermore, the cumulative sum, the classicalEWMA and the modified EWMA control charts are compared and the results show that the latter is better for smalland intermediate shift sizes. In addition, the explicit formula is successfully applied to real-world data in the healthfield as COVID-19 data in Thailand and Singapore.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the National Science,Research and Innovation Fund(NSRF)King Mongkuts University of Technology North Bangkok under contract no.KMUTNB-FF-68-B-08.
文摘This study aims to examine the explicit solution for calculating the Average Run Length(ARL)on the triple exponentially weighted moving average(TEWMA)control chart applied to autoregressive model(AR(p)),where AR(p)is an autoregressive model of order p,representing a time series with dependencies on its p previous values.Additionally,the study evaluates the accuracy of both explicit and numerical integral equation(NIE)solutions for AR(p)using the TEWMA control chart,focusing on the absolute percentage relative error.The results indicate that the explicit and approximate solutions are in close agreement.Furthermore,the study investigates the performance of exponentially weighted moving average(EWMA)and TEWMA control charts in detecting changes in the process,using the relative mean index(RMI)as a measure.The findings demonstrate that the TEWMA control chart outperforms the EWMA control chart in detecting process changes,especially when the value ofλis sufficiently large.In addition,an analysis using historical data from the SET index between January 2024 and May 2024 and historical data of global annual plastic production,the results of both data sets also emphasize the superior performance of the TEWMA control chart.
基金partially funded by the International Foundation for Science(Grant No:I-1-D-6066-1).
文摘Stem volume estimation is crucial in forest ecology and management,particularly for timber harvesting strategies and carbon stock assessments.This study aimed to develop a variable-exponent taper equation specifically tailored to savanna tree species using close-range photogrammetry(CRP)data and to evaluate its performance against conventional volume equations for stem volume estimation.A dataset of 30 trees across five dominant savanna species was used to fit the taper model,which was validated using a separate dataset of 322 trees from 14 species.The results demonstrated significant improvements in volume estimation accuracy when using the taper equation.At the tree level,the root mean square error(RMSE)decreased by 47%,from 598 to 319 dm^(3),and the mean absolute bias(MAB)by 48%,from 328 to 172 dm3,compared to volume equations.Similarly,at the plot level,RMSE was reduced by 42% and MAB by 40%.The model performed well for species with regular forms.However,species with irregular tapers exhibited higher errors,reflecting the challenges of modeling stem forms of mixed species.The use of CRP proved valuable,providing high-resolution diameter measurements that improved model parameterization.This study underscores the importance of advanced data collection methods for enhancing taper model accuracy and suggests that further species-specific adjustments are needed to improve performance for species with irregular forms.The findings support the broader application of taper equations for improving stem volume estimates in savanna ecosystems,contributing to better forest management and resource monitoring practices.
基金financially by the National Research Council of Thailand(NRCT)under Contract No.N42A670894.
文摘This study presents an innovative development of the exponentially weighted moving average(EWMA)control chart,explicitly adapted for the examination of time series data distinguished by seasonal autoregressive moving average behavior—SARMA(1,1)L under exponential white noise.Unlike previous works that rely on simplified models such as AR(1)or assume independence,this research derives for the first time an exact two-sided Average Run Length(ARL)formula for theModified EWMAchart under SARMA(1,1)L conditions,using a mathematically rigorous Fredholm integral approach.The derived formulas are validated against numerical integral equation(NIE)solutions,showing strong agreement and significantly reduced computational burden.Additionally,a performance comparison index(PCI)is introduced to assess the chart’s detection capability.Results demonstrate that the proposed method exhibits superior sensitivity to mean shifts in autocorrelated environments,outperforming existing approaches.The findings offer a new,efficient framework for real-time quality control in complex seasonal processes,with potential applications in environmental monitoring and intelligent manufacturing systems.
文摘In the past decade,financial institutions have invested significant efforts in the development of accurate analytical credit scoring models.The evidence suggests that even small improvements in the accuracy of existing credit-scoring models may optimize profits while effectively managing risk exposure.Despite continuing efforts,the majority of existing credit scoring models still include some judgment-based assumptions that are sometimes supported by the significant findings of previous studies but are not validated using the institution’s internal data.We argue that current studies related to the development of credit scoring models have largely ignored recent developments in statistical methods for sufficient dimension reduction.To contribute to the field of financial innovation,this study proposes a Dimension Reduction Assisted Credit Scoring(DRA-CS)method via distance covariance-based sufficient dimension reduction(DCOV-SDR)in Majorization-Minimization(MM)algorithm.First,in the presence of a large number of variables,the DRA-CS method results in greater dimension reduction and better prediction accuracy than the other methods used for dimension reduction.Second,when the DRA-CS method is employed with logistic regression,it outperforms existing methods based on different variable selection techniques.This study argues that the DRA-CS method should be used by financial institutions as a financial innovation tool to analyze high-dimensional customer datasets and improve the accuracy of existing credit scoring methods.
基金supported by the International Joint Research Grant by Yonsei Graduate School(Kwangwon Ahn)the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(RS-2025-16067531:Kwangwon Ahn)+2 种基金the NRF of Korea grant funded by the Korea government(MSIT)(2020R1A2C1A01005949,RS-2023-00217705:Taeyoung Park)the MSIT(Ministry of Science and ICT)Korea,under the ICAN(ICT Challenge and Advanced Network of HRD)support program(RS-2023-00259934:Taeyoung Park)supervised by the IITP(Institute for Information&Communications Technology Planning&Evaluation)the Son Jiho Research Grant of Yonsei University(2023-22-0006:Taeyoung Park).
文摘Put options are known to be priced unusually high in the market,which we refer to as the overpriced put puzzle.This study proposes a quantum model(QM)that can explain such high put option prices as fair prices.Starting from a stochastic differential equation of stock returns,we convert the Fokker–Planck equation into the Schr鰀inger equation.To model the market force that always draws excess returns back to equilibrium,we specify a diffusion process corresponding to a QM with a delta potential.The results demonstrate that stock returns follow a Laplace distribution and exhibit power law in the tail.We then construct a closed-form solution for European put option pricing,determining that our model better explains the returns of the S&P 500 index and its corresponding put option prices than do geometric Brownian motion-based models.This study has significant implications for investors and risk managers,presenting a model that can potentially improve derivative pricing.Future studies can generalize the model assumptions by introducing asymmetric potential drawing back excess returns to equilibrium.
基金supported by the Fundamental Research Funds for the Central Universities(Grant No.D5000230061)the Guangdong Basic and Applied Basic Research Foundation(Grant No.2025A1515011192).
文摘This study explores the magnetohydrodynamic(MHD)boundary layer flow of a water-based Boger nanofluid over a stretching sheet,with particular focus on the influences of nanoparticle diameter,nanolayer effects,and thermal radiation.The primary aim is to examine how variations in nanoparticle size and nanolayer thickness affect the hydrothermal behavior of the nanofluid.The model also incorporates the contributions of viscous dissipation and Joule heating within the heat transfer equation.The governing momentum and energy equations are converted into dimensionless partial differential equations(PDEs)using appropriate similarity variables and are numerically solved using the finite element method(FEM)implemented in MATLAB.Extensive validation of this method confirms its reliability and accuracy in numerical solutions.The findings reveal that increasing the diameter of copper nanoparticles significantly enhances the velocity profile,with a more pronounced effect observed at wider inter-particle spacings.A higher solvent volume fraction leads to decreased velocity and temperature distributions,while a greater relaxation time ratio improves velocity and temperature profiles due to the increased elastic response of the fluid.Moreover,enhancements in the magnetic parameter,thermal radiation,and Eckert number lead to an elevation in temperature profiles.Furthermore,higher nanolayer thickness reduces the temperature profile,whereas particle radius yields the opposite outcome.
文摘The migration of tasks aided by machine learning(ML)predictions IN(DPM)is a system-level design technique that is used to reduce energy by enhancing the overall performance of the processor.In this paper,we address the issue of system-level higher task dissipation during the execution of parallel workloads with common deadlines by introducing a machine learning-based framework that includes task migration using energy-efficient earliest deadline first scheduling(EA-EDF).ML-based EA-EDF enhances the overall throughput and optimizes the energy to avoid delay and performance degradation in a multiprocessor system.The proposed system model allocates processors to the ready task set in such a way that their deadlines are guaranteed.A full task migration policy is also integrated to ensure proper task mapping that ensures inter-process linkage among the arrived tasks with the same deadlines.The execution of a task can halt on one CPU and reschedule the execution on a different processor to avoid delay and ensure meeting the deadline.Our approach shows promising potential for machine-learning-based schedulability analysis enables a comparison between different ML models and shows a promising reduction in energy as compared with other ML-aware task migration techniques for SoC like Multi-Layer Feed-Forward Neural Networks(MLFNN)based on convolutional neural network(CNN),Random Forest(RF)and Deep learning(DL)algorithm.The Simulations are conducted using super pipelined microarchitecture of advanced micro devices(AMD)XScale PXA270 using instruction and data cache per core 32 Kbyte I-cache and 32 Kbyte D-cache on various utilization factors(u_(i))12%,31%and 50%.The proposed approach consumes 5.3%less energy when almost half of the CPU is running and on a lower workload consumes 1.04%less energy.The proposed design accumulatively gives significant improvements by reducing the energy dissipation on three clock rates by 4.41%,on 624 MHz by 5.4%and 5.9%on applications operating on 416 and 312 MHz standard operating frequencies.
基金Financial support from the Deutsche Forschungsgemeinschaft via the IRTG 1792"High-dimensional,Non-stationary Time Series",Humboldt-Universitätzu Berlin,is gratefully acknowledgedfunding from the European Union’s"FIN-TECH:A Financial supervision and Technology compliance training programme"under the Grant agreement No 825215.
文摘We construct an exchange-traded fund(ETF)based on the CRyptocurrency IndeX(CRIX),which closely maps nonstationary cryptocurrency(CC)dynamics by adapting the weights of its constituents dynamically.Our scenario analysis considers the fee schedules of regulated CC exchanges,spreads obtained from order book data,and investment in-&outflows to the ETF are modelled stochastically.The scenario analysis yields valuable insights into the mechanisms,costs,and risks of this innovative financial product:i)although the composition of the CRIX ETF changes frequently(from 5 to 30 constituents),it remains robust in its core,as the weights of Bitcoin(BTC)and Ethereum(ETH)are robust over time;ii)on average,5.2%needs to be rebalanced on the rebalancing dates;iii)trading costs are low compared with traditional assets;iv)the liquidity of the CC sector increases significantly during the analysis period;spreads occur,especially for altcoins and increase with the size of the transactions.However,because BTC and ETH are the most affected by rebalancing,the cost of spreads remains limited.
基金the National Basic Research Program of China (No 2005CB522404 and 2006CB910506)the Program for Changjiang Scholars and Innovative Research Team (PCSIRT) in Universities (No IRT0519)the National Natural Science Founda-tion of China (No 30771232 and 30671184)
文摘Mesenchymal stem cells （MSCs） of nonembryonic origins possess the proliferation and multi-lineage differentiation potentials. It has been established that epigenetic mechanisms could be critical for determining the fate of stem cells, and MSCs derived from different origins exhibited different expression profiles individually to a certain extent. In this study, ChiP-on-chip was used to generate genome-wide histone H3-Lys9 acetylation and dimethylation profiles at gene promoters in human bone marrow MSCs. We showed that modifications of histone H3-Lys9 at gene promoters correlated well with mRNA expression in human bone marrow MSCs. Functional analysis revealed that many key cellular pathways in human bone marrow MSC self-renewal, such as the canonical signaling pathways, cell cycle pathways and cytokine related pathways may be regulated by H3-Lys9 modifications. These data suggest that gene activation and silencing affected by H3-Lys9 acetylation and dimethylation, respectively, may be essential to the maintenance of human bone marrow MSC self-renewal and multi-potency.
基金National Key Basic Research Program of China (973 Program) under Grant No.2012CB315802 and No.2013CB329102.National Natural Science Foundation of China under Grant No.61171102 and No.61132001.New generation broadband wireless mobile communication network Key Projects for Science and Technology Development under Grant No.2011ZX03002-002-01,Beijing Nova Program under Grant No.2008B50 and Beijing Higher Education Young Elite Teacher Project under Grant No.YETP0478
文摘Differently from the general online social network(OSN),locationbased mobile social network(LMSN),which seamlessly integrates mobile computing and social computing technologies,has unique characteristics of temporal,spatial and social correlation.Recommending friends instantly based on current location of users in the real world has become increasingly popular in LMSN.However,the existing friend recommendation methods based on topological structures of a social network or non-topological information such as similar user profiles cannot well address the instant making friends in the real world.In this article,we analyze users' check-in behavior in a real LMSN site named Gowalla.According to this analysis,we present an approach of recommending friends instantly for LMSN users by considering the real-time physical location proximity,offline behavior similarity and friendship network information in the virtual community simultaneously.This approach effectively bridges the gap between the offline behavior of users in the real world and online friendship network information in the virtual community.Finally,we use the real user check-in dataset of Gowalla to verify the effectiveness of our approach.
文摘We construct the membership functions of the fuzzy objective values of a controllable queueing model,in which cost elements,arrival rate and service rate are all fuzzy numbers. Based on Zadeh's extension principle,a set of parametric nonlinear programs is developed to find the upper and lower bounds of the minimal average total cost per unit time at the possibility level. The membership functions of the minimal average total cost are further constructed using different values of the possibility level. A numerical example is solved successfully to illustrate the validity of the proposed approach. Because the object value is expressed and governed by the membership functions,the optimization problem in a fuzzy environment for the controllable queueing models is represented more accurately and analytical results are more useful for system designers and practitioners.
基金supported by NSFC of China Grant(11371085)the Fundamental Research Funds for the Central Universities(15CX08011A)
文摘This article addresses a stochastic ratio-dependent predator-prey system with Leslie-Gower and Holling type II schemes. Firstly, the existence of the global positive solution is shown by the comparison theorem of stochastic differential equations. Secondly, in the case of persistence, we prove that there exists a ergodic stationary distribution. Finally, numerical simulations for a hypothetical set of parameter values are presented to illustrate the analytical findings.
基金supported by NSFC of China(11371085)the Fundamental Research Funds for the Central Universities(15CX08011A),2016GXNSFBA380006 and KY2016YB370
文摘This paper is concerned with a stochastic HBV infection model with logistic growth. First, by constructing suitable stochastic Lyapunov functions, we establish sufficient conditions for the existence of ergodic stationary distribution of the solution to the HBV infection model. Then we obtain sufficient conditions for extinction of the disease. The stationary distribution shows that the disease can become persistent in vivo.
文摘Cover crops are the plants which are grown to improve soil fertility, prevent soil erosion, enrichment and protection of soil, and enhance nutrient and water availability, and quality of soil. Cover crops provide several benefits to soils used for agriculture production. Cover crops are helpful in increasing and sustaining microbial biodiversity in soils. We summarized the effect of several cover crops in soil properties such as soil moisture content, soil microbial activities, soil carbon sequestration, nitrate leaching, soil water, and soil health. Selection of cover crops usually depends on the primary benefits which are provided by cover crops. Other factors may also include weather conditions, time of sowing, either legume or non-legume and timing and method of killing of a cover crop. In recent times, cover crops are also used for mitigating climate change, suppressing weeds in crops and increasing exchangeable nutrients such as Mg2+ and K+. Cover crops are also found to be economical in long-term experiment studies. Although some limitations always come with several benefits. Cover crops have some problems including the method of killing, host for pathogens, regeneration, and not immediate benefits of using them. Despite the few limitations, cover crops improve the overall health of the soil and provide a sustainable environment for the main crops.
基金The research was funding by King Mongkut’s University of Technology North Bangkok Contract No.KMUTNB-61-GOV-03-23.
文摘Coronavirus disease 2019 outbreak has spread as a pandemic since the end of year 2019.This situation has been causing a lot of problems of human beings such as economic problems,health problems.The forecasting of the number of infectious people is required by the authorities of all countries including Southeast Asian countries to make a decision and control the outbreak.This research is to investigate the suitable forecasting model for the number of infectious people in Southeast Asian countries.A comparison of forecasting models between logistic growth curve which is symmetric and Gompertz growth curve which is asymmetric based on the maximumof Coefficient of Determination and theminimumof RootMean Squared Percentage Error is also proposed.The estimation of parameters of the forecasting models is evaluated by the least square method.In addition,spreading of the outbreak is estimated by the derivative of the number of cumulative cases.The findings show that Gompertz growth curve is a suitable forecasting model for Indonesia,Philippines,andMalaysia and logistic growth curve suits the other countries in South Asia.
文摘We compare the optimal operating cost of the two bicriterion policies, <p,T> and <p,N>, for an M/G/1 queueing system with second optional service, in which the length of the vacation period is randomly controlled either by the number of arrivals during the idle period or by a timer. After all the customers are served in the queue exhaustively, the server immediately takes a vacation and may operate <p,T> policy or <p,N> policy. For the two bicriterion policies, the total average cost function per unit time is developed to search the optimal stationary operating policies at a minimum cost. Based upon the optimal cost the explicit forms for joint optimum threshold values of (p,T) and (p,N) are obtained.
基金The research was supported by King Mongkut’s University of Technology North Bangkok Contract No.KMUTNB-62-KNOW-018.
文摘A modified exponentially weighted moving average (EWMA) scheme is one of the quality control charts suchthat this control chart can quickly detect a small shift. The average run length (ARL) is frequently used for theperformance evaluation on control charts. This paper proposes the explicit formula for evaluating the average runlength on a two-sided modified exponentially weighted moving average chart under the observations of a first-orderautoregressive process, referred to as AR(1) process, with an exponential white noise. The performance comparisonof the explicit formula and the numerical integral technique is carried out using the absolute relative change forchecking the correct formula and the CPU time for testing speed of calculation. The results show that the ARL ofthe explicit formula and the numerical integral equation method are hardly different, but this explicit formula ismuch faster for calculating the ARL and offered accurate values. Furthermore, the cumulative sum, the classicalEWMA and the modified EWMA control charts are compared and the results show that the latter is better for smalland intermediate shift sizes. In addition, the explicit formula is successfully applied to real-world data in the healthfield as COVID-19 data in Thailand and Singapore.
