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
Crop-yield is a crucial metric in agriculture,essential for effective sector management and improving the overall production process.This indicator is heavily influenced by numerous environmental factors,particularly ...Crop-yield is a crucial metric in agriculture,essential for effective sector management and improving the overall production process.This indicator is heavily influenced by numerous environmental factors,particularly those related to soil and climate,which present a challenging task due to the complex interactions involved.In this paper,we introduce a novel integrated neurosymbolic framework that combines knowledge-based approaches with sensor data for crop-yield prediction.This framework merges predictions from vectors generated by modeling environmental factors using a newly developed ontology focused on key elements and evaluates this ontology using quantitative methods,specifically representation learning techniques,along with predictions derived from remote sensing imagery.We tested our proposed methodology on a public dataset centered on corn,aiming to predict crop-yield.Our developed smart model achieved promising results in terms of crop-yield prediction,with a root mean squared error(RMSE)of 1.72,outperforming the baseline models.The ontologybased approach achieved an RMSE of 1.73,while the remote sensing-based method yielded an RMSE of 1.77.This confirms the superior performance of our proposed approach over those using single modalities.This in-tegrated neurosymbolic approach demonstrates that the fusion of statistical and symbolic artificial intelligence(AI)represents a significant advancement in agricultural applications.It is particularly effective for crop-yield prediction at the field scale,thus facilitating more informed decision-making in advanced agricultural prac-tices.Additionally,it is acknowledged that results might be further improved by incorporating more detailed ontological knowledge and testing the model with higher-resolution imagery to enhance prediction accuracy.展开更多
This report provides an overall assessment of land fragmentation problems in East Africa. Many parts of East Africa have become highly fragmented, putting development systems and activities in these areas at risk of c...This report provides an overall assessment of land fragmentation problems in East Africa. Many parts of East Africa have become highly fragmented, putting development systems and activities in these areas at risk of complete collapse. Land fragmentation occurs when land gets converted for agriculture, industrialization, or urbanization, invaded by non-local plants, or enclosed for individual use and by subdividing farmlands into subsequent smaller units called parcels with varying average farm sizes. Fragmentation results from inappropriate agricultural development processes and ineffective land use planning that fails to recognize how farmland is used, and the importance of its interconnected areas. Insecurity of tenure and resource rights are key factors in making this possible. Land fragmentation is one of the key reasons why the ability of most resources in East Africa becomes scarcer, and those remaining become “privatized” by more powerful community members—keen to maintain their access to them. Such individualistic attitudes are new and disadvantage the poorest even further by affecting the traditional customary safety nets and agricultural outputs. Neither the government nor customary governance systems effectively protect resource access for the poorest. This review summary report identifies the key causes, measures, and implications, government interventions, and the common remedies to land fragmentation problems in the East African Countries of Kenya, Uganda, Rwanda, and Tanzania including neighboring Ethiopia, and the Sudan. The findings indicated from 2005 to 2015, the population kept increasing for all the named countries in East Africa with Rwanda and Uganda having a substantial increase in population density. The study review further explores the trend in the performance of agriculture by average farm sizes within the intervals of five years by highlighting their strong linkages and found that the average farm size has declined drastically, especially for Kenya. This can only mean that small farms kept becoming smaller and smaller and that there were more small-scale farmers. The results further depicted that the major and commonly cultivated food crops among the East African countries include maize, sorghum, rice, cassava, sweet potatoes, bananas, Irish potatoes, beans, peas, etc., with maize yields (Mt/ha) in 2003 for Uganda being the highest (1.79 Mt/ha) and the lowest in Rwanda (0.77 Mt/ha) respectively. Therefore, from the review results, recommendations are being made as to how the negative impacts of land fragmentation on agricultural productivity can be reduced or mitigated. One way is by community sensitization and awareness about the importance of land consolidation and its proposition on farm productivity.展开更多
This study presents the design of a modified attributed control chart based on a double sampling(DS)np chart applied in combination with generalized multiple dependent state(GMDS)sampling to monitor the mean life of t...This study presents the design of a modified attributed control chart based on a double sampling(DS)np chart applied in combination with generalized multiple dependent state(GMDS)sampling to monitor the mean life of the product based on the time truncated life test employing theWeibull distribution.The control chart developed supports the examination of the mean lifespan variation for a particular product in the process of manufacturing.Three control limit levels are used:the warning control limit,inner control limit,and outer control limit.Together,they enhance the capability for variation detection.A genetic algorithm can be used for optimization during the in-control process,whereby the optimal parameters can be established for the proposed control chart.The control chart performance is assessed using the average run length,while the influence of the model parameters upon the control chart solution is assessed via sensitivity analysis based on an orthogonal experimental design withmultiple linear regression.A comparative study was conducted based on the out-of-control average run length,in which the developed control chart offered greater sensitivity in the detection of process shifts while making use of smaller samples on average than is the case for existing control charts.Finally,to exhibit the utility of the developed control chart,this paper presents its application using simulated data with parameters drawn from the real set of data.展开更多
In this paper, we intend to consider a kind of nonlinear Klein-Gordon equation coupled with Born-Infeld theory. By using critical point theory and the method of Nehari manifold, we obtain two existing results of infin...In this paper, we intend to consider a kind of nonlinear Klein-Gordon equation coupled with Born-Infeld theory. By using critical point theory and the method of Nehari manifold, we obtain two existing results of infinitely many high-energy radial solutions and a ground-state solution for this kind of system, which improve and generalize some related results in the literature.展开更多
Mediterranean anemia is a genetic disease that currently relies heavily on expert clinical experience to determine whether patients are affected. This method is overly reliant on expert experience and is not precise e...Mediterranean anemia is a genetic disease that currently relies heavily on expert clinical experience to determine whether patients are affected. This method is overly reliant on expert experience and is not precise enough. This paper proposes two modeling methods to predict whether patients have Mediterranean anemia. The first method involves using Principal Component Analysis (PCA) to reduce the dimensionality of the data, followed by logistic regression modeling (PCA-LR) on the reduced dataset. The second method involves building a Partial Least Squares Regression (PLS) model. Experimental results show that the prediction accuracy of the PCA-LR model is 87.5% (degree = 2, λ=4), and the prediction accuracy of the PLS model is 92.5% (ncomp = 4), indicating good predictive performance of the models.展开更多
The aim of this paper is to broaden the application of Stochastic Configuration Network (SCN) in the semi-supervised domain by utilizing common unlabeled data in daily life. It can enhance the classification accuracy ...The aim of this paper is to broaden the application of Stochastic Configuration Network (SCN) in the semi-supervised domain by utilizing common unlabeled data in daily life. It can enhance the classification accuracy of decentralized SCN algorithms while effectively protecting user privacy. To this end, we propose a decentralized semi-supervised learning algorithm for SCN, called DMT-SCN, which introduces teacher and student models by combining the idea of consistency regularization to improve the response speed of model iterations. In order to reduce the possible negative impact of unsupervised data on the model, we purposely change the way of adding noise to the unlabeled data. Simulation results show that the algorithm can effectively utilize unlabeled data to improve the classification accuracy of SCN training and is robust under different ground simulation environments.展开更多
In the contemporary era, the proliferation of information technology has led to an unprecedented surge in data generation, with this data being dispersed across a multitude of mobile devices. Facing these situations a...In the contemporary era, the proliferation of information technology has led to an unprecedented surge in data generation, with this data being dispersed across a multitude of mobile devices. Facing these situations and the training of deep learning model that needs great computing power support, the distributed algorithm that can carry out multi-party joint modeling has attracted everyone’s attention. The distributed training mode relieves the huge pressure of centralized model on computer computing power and communication. However, most distributed algorithms currently work in a master-slave mode, often including a central server for coordination, which to some extent will cause communication pressure, data leakage, privacy violations and other issues. To solve these problems, a decentralized fully distributed algorithm based on deep random weight neural network is proposed. The algorithm decomposes the original objective function into several sub-problems under consistency constraints, combines the decentralized average consensus (DAC) and alternating direction method of multipliers (ADMM), and achieves the goal of joint modeling and training through local calculation and communication of each node. Finally, we compare the proposed decentralized algorithm with several centralized deep neural networks with random weights, and experimental results demonstrate the effectiveness of the proposed algorithm.展开更多
Phishing is one of the most common threats on the Internet. Traditionally, detection methods have relied on blacklists and heuristic rules, but these approaches are showing their limitations in the face of rapidly evo...Phishing is one of the most common threats on the Internet. Traditionally, detection methods have relied on blacklists and heuristic rules, but these approaches are showing their limitations in the face of rapidly evolving attack techniques. Artificial Intelligence (AI) offers promising solutions for improving phishing detection, prediction and prevention. In our study, we analyzed three supervised machine learning classifiers and one deep learning classifier for detecting and predicting phishing websites: Naive Bayes, Decision Tree, Gradient Boosting and Multi-Layer Perceptron. The results showed that the Gradient Boosting Classifier performed best, with a precision of 96.2%, a F1-score of 96.6%, recall and precision of 99.9% in all classes, and a mean absolute error (MAE) of just 0.002. Closely followed by the Gradient Boosting Classifier with a precision of 96.2% and a score of 96.6%. In contrast, Naive Bayes and the Decision Tree showed a lower accuracy rate. These results underline the importance of high accuracy in these models to reduce the risk associated with malicious attachments and reinforce security measures in this area of research.展开更多
Objective Little is known about the association between whole-blood nicotinamide adenine dinucleotide(NAD^(+))levels and nabothian cysts.This study aimed to assess the association between NAD^(+)levels and nabothian c...Objective Little is known about the association between whole-blood nicotinamide adenine dinucleotide(NAD^(+))levels and nabothian cysts.This study aimed to assess the association between NAD^(+)levels and nabothian cysts in healthy Chinese women.Methods Multivariate logistic regression analysis was performed to analyze the association between NAD^(+)levels and nabothian cysts.Results The mean age was 43.0±11.5 years,and the mean level of NAD^(+)was 31.3±5.3μmol/L.Nabothian cysts occurred in 184(27.7%)participants,with single and multiple cysts in 100(15.0%)and84(12.6%)participants,respectively.The total nabothian cyst prevalence gradually decreased from37.4%to 21.6%from Q1 to Q4 of NAD^(+)and the prevalence of single and multiple nabothian cysts also decreased across the NAD^(+)quartiles.As compared with the highest NAD^(+)quartile(≥34.4μmol/L),the adjusted odds ratios with 95%confidence interval of the NAD^(+)Q1 was 1.89(1.14–3.14)for total nabothian cysts.The risk of total and single nabothian cysts linearly decreased with increasing NAD^(+)levels,while the risk of multiple nabothian cysts decreased more rapidly at NAD^(+)levels of 28.0 to35.0μmol/L.Conclusion:Low NAD^(+)levels were associated with an increased risk of total and multiple nabothian cysts.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partially funded by the JSPS KAKENHI Grant Number JP22K18004.
文摘Crop-yield is a crucial metric in agriculture,essential for effective sector management and improving the overall production process.This indicator is heavily influenced by numerous environmental factors,particularly those related to soil and climate,which present a challenging task due to the complex interactions involved.In this paper,we introduce a novel integrated neurosymbolic framework that combines knowledge-based approaches with sensor data for crop-yield prediction.This framework merges predictions from vectors generated by modeling environmental factors using a newly developed ontology focused on key elements and evaluates this ontology using quantitative methods,specifically representation learning techniques,along with predictions derived from remote sensing imagery.We tested our proposed methodology on a public dataset centered on corn,aiming to predict crop-yield.Our developed smart model achieved promising results in terms of crop-yield prediction,with a root mean squared error(RMSE)of 1.72,outperforming the baseline models.The ontologybased approach achieved an RMSE of 1.73,while the remote sensing-based method yielded an RMSE of 1.77.This confirms the superior performance of our proposed approach over those using single modalities.This in-tegrated neurosymbolic approach demonstrates that the fusion of statistical and symbolic artificial intelligence(AI)represents a significant advancement in agricultural applications.It is particularly effective for crop-yield prediction at the field scale,thus facilitating more informed decision-making in advanced agricultural prac-tices.Additionally,it is acknowledged that results might be further improved by incorporating more detailed ontological knowledge and testing the model with higher-resolution imagery to enhance prediction accuracy.
文摘This report provides an overall assessment of land fragmentation problems in East Africa. Many parts of East Africa have become highly fragmented, putting development systems and activities in these areas at risk of complete collapse. Land fragmentation occurs when land gets converted for agriculture, industrialization, or urbanization, invaded by non-local plants, or enclosed for individual use and by subdividing farmlands into subsequent smaller units called parcels with varying average farm sizes. Fragmentation results from inappropriate agricultural development processes and ineffective land use planning that fails to recognize how farmland is used, and the importance of its interconnected areas. Insecurity of tenure and resource rights are key factors in making this possible. Land fragmentation is one of the key reasons why the ability of most resources in East Africa becomes scarcer, and those remaining become “privatized” by more powerful community members—keen to maintain their access to them. Such individualistic attitudes are new and disadvantage the poorest even further by affecting the traditional customary safety nets and agricultural outputs. Neither the government nor customary governance systems effectively protect resource access for the poorest. This review summary report identifies the key causes, measures, and implications, government interventions, and the common remedies to land fragmentation problems in the East African Countries of Kenya, Uganda, Rwanda, and Tanzania including neighboring Ethiopia, and the Sudan. The findings indicated from 2005 to 2015, the population kept increasing for all the named countries in East Africa with Rwanda and Uganda having a substantial increase in population density. The study review further explores the trend in the performance of agriculture by average farm sizes within the intervals of five years by highlighting their strong linkages and found that the average farm size has declined drastically, especially for Kenya. This can only mean that small farms kept becoming smaller and smaller and that there were more small-scale farmers. The results further depicted that the major and commonly cultivated food crops among the East African countries include maize, sorghum, rice, cassava, sweet potatoes, bananas, Irish potatoes, beans, peas, etc., with maize yields (Mt/ha) in 2003 for Uganda being the highest (1.79 Mt/ha) and the lowest in Rwanda (0.77 Mt/ha) respectively. Therefore, from the review results, recommendations are being made as to how the negative impacts of land fragmentation on agricultural productivity can be reduced or mitigated. One way is by community sensitization and awareness about the importance of land consolidation and its proposition on farm productivity.
基金the Science,Research and Innovation Promotion Funding(TSRI)(Grant No.FRB660012/0168)managed under Rajamangala University of Technology Thanyaburi(FRB66E0646O.4).
文摘This study presents the design of a modified attributed control chart based on a double sampling(DS)np chart applied in combination with generalized multiple dependent state(GMDS)sampling to monitor the mean life of the product based on the time truncated life test employing theWeibull distribution.The control chart developed supports the examination of the mean lifespan variation for a particular product in the process of manufacturing.Three control limit levels are used:the warning control limit,inner control limit,and outer control limit.Together,they enhance the capability for variation detection.A genetic algorithm can be used for optimization during the in-control process,whereby the optimal parameters can be established for the proposed control chart.The control chart performance is assessed using the average run length,while the influence of the model parameters upon the control chart solution is assessed via sensitivity analysis based on an orthogonal experimental design withmultiple linear regression.A comparative study was conducted based on the out-of-control average run length,in which the developed control chart offered greater sensitivity in the detection of process shifts while making use of smaller samples on average than is the case for existing control charts.Finally,to exhibit the utility of the developed control chart,this paper presents its application using simulated data with parameters drawn from the real set of data.
文摘In this paper, we intend to consider a kind of nonlinear Klein-Gordon equation coupled with Born-Infeld theory. By using critical point theory and the method of Nehari manifold, we obtain two existing results of infinitely many high-energy radial solutions and a ground-state solution for this kind of system, which improve and generalize some related results in the literature.
文摘Mediterranean anemia is a genetic disease that currently relies heavily on expert clinical experience to determine whether patients are affected. This method is overly reliant on expert experience and is not precise enough. This paper proposes two modeling methods to predict whether patients have Mediterranean anemia. The first method involves using Principal Component Analysis (PCA) to reduce the dimensionality of the data, followed by logistic regression modeling (PCA-LR) on the reduced dataset. The second method involves building a Partial Least Squares Regression (PLS) model. Experimental results show that the prediction accuracy of the PCA-LR model is 87.5% (degree = 2, λ=4), and the prediction accuracy of the PLS model is 92.5% (ncomp = 4), indicating good predictive performance of the models.
文摘The aim of this paper is to broaden the application of Stochastic Configuration Network (SCN) in the semi-supervised domain by utilizing common unlabeled data in daily life. It can enhance the classification accuracy of decentralized SCN algorithms while effectively protecting user privacy. To this end, we propose a decentralized semi-supervised learning algorithm for SCN, called DMT-SCN, which introduces teacher and student models by combining the idea of consistency regularization to improve the response speed of model iterations. In order to reduce the possible negative impact of unsupervised data on the model, we purposely change the way of adding noise to the unlabeled data. Simulation results show that the algorithm can effectively utilize unlabeled data to improve the classification accuracy of SCN training and is robust under different ground simulation environments.
文摘In the contemporary era, the proliferation of information technology has led to an unprecedented surge in data generation, with this data being dispersed across a multitude of mobile devices. Facing these situations and the training of deep learning model that needs great computing power support, the distributed algorithm that can carry out multi-party joint modeling has attracted everyone’s attention. The distributed training mode relieves the huge pressure of centralized model on computer computing power and communication. However, most distributed algorithms currently work in a master-slave mode, often including a central server for coordination, which to some extent will cause communication pressure, data leakage, privacy violations and other issues. To solve these problems, a decentralized fully distributed algorithm based on deep random weight neural network is proposed. The algorithm decomposes the original objective function into several sub-problems under consistency constraints, combines the decentralized average consensus (DAC) and alternating direction method of multipliers (ADMM), and achieves the goal of joint modeling and training through local calculation and communication of each node. Finally, we compare the proposed decentralized algorithm with several centralized deep neural networks with random weights, and experimental results demonstrate the effectiveness of the proposed algorithm.
文摘Phishing is one of the most common threats on the Internet. Traditionally, detection methods have relied on blacklists and heuristic rules, but these approaches are showing their limitations in the face of rapidly evolving attack techniques. Artificial Intelligence (AI) offers promising solutions for improving phishing detection, prediction and prevention. In our study, we analyzed three supervised machine learning classifiers and one deep learning classifier for detecting and predicting phishing websites: Naive Bayes, Decision Tree, Gradient Boosting and Multi-Layer Perceptron. The results showed that the Gradient Boosting Classifier performed best, with a precision of 96.2%, a F1-score of 96.6%, recall and precision of 99.9% in all classes, and a mean absolute error (MAE) of just 0.002. Closely followed by the Gradient Boosting Classifier with a precision of 96.2% and a score of 96.6%. In contrast, Naive Bayes and the Decision Tree showed a lower accuracy rate. These results underline the importance of high accuracy in these models to reduce the risk associated with malicious attachments and reinforce security measures in this area of research.
基金supported by grants from the NSFC-Regional Innovation and Development Joint Fund(No.U22A20364)the National Key R&D Program of China(No.2021YFC2500500)the National Natural Science Foundation of China(No.81973112,No.92049302)。
文摘Objective Little is known about the association between whole-blood nicotinamide adenine dinucleotide(NAD^(+))levels and nabothian cysts.This study aimed to assess the association between NAD^(+)levels and nabothian cysts in healthy Chinese women.Methods Multivariate logistic regression analysis was performed to analyze the association between NAD^(+)levels and nabothian cysts.Results The mean age was 43.0±11.5 years,and the mean level of NAD^(+)was 31.3±5.3μmol/L.Nabothian cysts occurred in 184(27.7%)participants,with single and multiple cysts in 100(15.0%)and84(12.6%)participants,respectively.The total nabothian cyst prevalence gradually decreased from37.4%to 21.6%from Q1 to Q4 of NAD^(+)and the prevalence of single and multiple nabothian cysts also decreased across the NAD^(+)quartiles.As compared with the highest NAD^(+)quartile(≥34.4μmol/L),the adjusted odds ratios with 95%confidence interval of the NAD^(+)Q1 was 1.89(1.14–3.14)for total nabothian cysts.The risk of total and single nabothian cysts linearly decreased with increasing NAD^(+)levels,while the risk of multiple nabothian cysts decreased more rapidly at NAD^(+)levels of 28.0 to35.0μmol/L.Conclusion:Low NAD^(+)levels were associated with an increased risk of total and multiple nabothian cysts.
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.
