Pavement management systems(PMS)are used by transportation government agencies to promote sustainable development and to keep road pavement conditions above the minimum performance levels at a reasonable cost.To accom...Pavement management systems(PMS)are used by transportation government agencies to promote sustainable development and to keep road pavement conditions above the minimum performance levels at a reasonable cost.To accomplish this objective,the pavement condition is monitored to predict deterioration and determine the need for maintenance or rehabilitation at the appropriate time.The pavement condition index(PCI)is a commonly usedmetric to evaluate the pavement's performance.This research aims to create and evaluate prediction models for PCI values using multiple linear regression(MLR),artificial neural networks(ANN),and fuzzy logic inference(FIS)models for flexible pavement sections.The authors collected field data spans for 2018 and 2021.Eight pavement distress factors were considered inputs for predicting PCI values,such as rutting,fatigue cracking,block cracking,longitudinal cracking,transverse cracking,patching,potholes,and delamination.This study evaluates the performance of the three techniques based on the coefficient of determination,root mean squared error(RMSE),and mean absolute error(MAE).The results show that the R2 values of the ANN models increased by 51.32%,2.02%,36.55%,and 3.02%compared toMLR and FIS(2018 and 2021).The error in the PCI values predicted by the ANNmodel was significantly lower than the errors in the prediction by the FIS and MLR models.展开更多
The economic development needs of developing countries require capital accumulation, which is no longer an easy task, even for industrialized countries. Although borrowing remains an important alternative, it has prov...The economic development needs of developing countries require capital accumulation, which is no longer an easy task, even for industrialized countries. Although borrowing remains an important alternative, it has proved to be an expensive method in the long run. Consequently, to attract foreign direct investment （FDI）, developing countries have been liberalizing their economies, which is expected to contribute to job creation and income generation. Libya declared its intention to liberalize its economy and to integrate into the global economy in order to achieve comprehensive development. This study investigates and explores the condition of the Libyan business environment in relation to foreign and joint companies, particularly in the non-oil sectors. This paper aims to investigate whether or not the Libyan business environment is appropriate to attract foreign companies, particularly in the non-hydrocarbon sectors. The method used in this paper is based on creating Porter model of competitive advantage of in relation to attract FDI. The paper reveals clearly that apart from substantial oil reserves, Libya is rich in other resources. Despite these positive advantages, there are numerous obstacles and shortcomings associated with the Libyan business environment. It discovered that the general structure and policies in relation to the Libyan business environment still require considerable attention to bring about the political and administrative stability, as well as the stability of laws and regulations. Furthermore, intensive media campaigns need to be launched with all the necessary legal and political guarantees for attracting FDI into the country.展开更多
Because of the presence of sporadic high-intensity measurement noise （outliers）, an adaptive algorithm for the robust estimation of parameters of linear dynamic discrete-time systems is proposed in this paper. First...Because of the presence of sporadic high-intensity measurement noise （outliers）, an adaptive algorithm for the robust estimation of parameters of linear dynamic discrete-time systems is proposed in this paper. First, the sorted data versus the normal quantiles is plotted, called QQ-plot. Next, the e-contaminated normal distribution of noise is adopted. Then, a data classification procedure based on the QQ-plot approach, combined with the robustified data winsorization technique, is developed; the estimation of the unknown noise statistical parameters is solved. Moreover, an iterative procedure for estimating the contamination degree ~＇, which originated from an ML classification, is also proposed. Thus, an ^-contaminated noise distribution is estimated and, the suboptimal maximum likelihood criterion is defined, and the system-parameter estimation problem is solved robustly, using the proposed recursive robust parameter estimation scheme. Finally, these parameters are used to estimate water level in the steam drum and residual of the steam-drum water level sensor.展开更多
文摘Pavement management systems(PMS)are used by transportation government agencies to promote sustainable development and to keep road pavement conditions above the minimum performance levels at a reasonable cost.To accomplish this objective,the pavement condition is monitored to predict deterioration and determine the need for maintenance or rehabilitation at the appropriate time.The pavement condition index(PCI)is a commonly usedmetric to evaluate the pavement's performance.This research aims to create and evaluate prediction models for PCI values using multiple linear regression(MLR),artificial neural networks(ANN),and fuzzy logic inference(FIS)models for flexible pavement sections.The authors collected field data spans for 2018 and 2021.Eight pavement distress factors were considered inputs for predicting PCI values,such as rutting,fatigue cracking,block cracking,longitudinal cracking,transverse cracking,patching,potholes,and delamination.This study evaluates the performance of the three techniques based on the coefficient of determination,root mean squared error(RMSE),and mean absolute error(MAE).The results show that the R2 values of the ANN models increased by 51.32%,2.02%,36.55%,and 3.02%compared toMLR and FIS(2018 and 2021).The error in the PCI values predicted by the ANNmodel was significantly lower than the errors in the prediction by the FIS and MLR models.
文摘The economic development needs of developing countries require capital accumulation, which is no longer an easy task, even for industrialized countries. Although borrowing remains an important alternative, it has proved to be an expensive method in the long run. Consequently, to attract foreign direct investment （FDI）, developing countries have been liberalizing their economies, which is expected to contribute to job creation and income generation. Libya declared its intention to liberalize its economy and to integrate into the global economy in order to achieve comprehensive development. This study investigates and explores the condition of the Libyan business environment in relation to foreign and joint companies, particularly in the non-oil sectors. This paper aims to investigate whether or not the Libyan business environment is appropriate to attract foreign companies, particularly in the non-hydrocarbon sectors. The method used in this paper is based on creating Porter model of competitive advantage of in relation to attract FDI. The paper reveals clearly that apart from substantial oil reserves, Libya is rich in other resources. Despite these positive advantages, there are numerous obstacles and shortcomings associated with the Libyan business environment. It discovered that the general structure and policies in relation to the Libyan business environment still require considerable attention to bring about the political and administrative stability, as well as the stability of laws and regulations. Furthermore, intensive media campaigns need to be launched with all the necessary legal and political guarantees for attracting FDI into the country.
文摘Because of the presence of sporadic high-intensity measurement noise （outliers）, an adaptive algorithm for the robust estimation of parameters of linear dynamic discrete-time systems is proposed in this paper. First, the sorted data versus the normal quantiles is plotted, called QQ-plot. Next, the e-contaminated normal distribution of noise is adopted. Then, a data classification procedure based on the QQ-plot approach, combined with the robustified data winsorization technique, is developed; the estimation of the unknown noise statistical parameters is solved. Moreover, an iterative procedure for estimating the contamination degree ~＇, which originated from an ML classification, is also proposed. Thus, an ^-contaminated noise distribution is estimated and, the suboptimal maximum likelihood criterion is defined, and the system-parameter estimation problem is solved robustly, using the proposed recursive robust parameter estimation scheme. Finally, these parameters are used to estimate water level in the steam drum and residual of the steam-drum water level sensor.
