In this paper, auxiliary information is used to determine an estimator of finite population total using nonparametric regression under stratified random sampling. To achieve this, a model-based approach is adopted by ...In this paper, auxiliary information is used to determine an estimator of finite population total using nonparametric regression under stratified random sampling. To achieve this, a model-based approach is adopted by making use of the local polynomial regression estimation to predict the nonsampled values of the survey variable y. The performance of the proposed estimator is investigated against some design-based and model-based regression estimators. The simulation experiments show that the resulting estimator exhibits good properties. Generally, good confidence intervals are seen for the nonparametric regression estimators, and use of the proposed estimator leads to relatively smaller values of RE compared to other estimators.展开更多
Studies on drug combinations are becoming more and more popular in the past few decades, with the development of computer and algorithms. One of the most common methods in optimizing drug combinations is regression of...Studies on drug combinations are becoming more and more popular in the past few decades, with the development of computer and algorithms. One of the most common methods in optimizing drug combinations is regression of a polynomial model based on certain number of experimental observations. In this paper, we study how to determine the degree of polynomials in different circumstances of drug combination optimization. Using cross-validation, we have found that in most cases, a high degree results in failures of accurate prediction, named overfitting. An anti-noise test has also revealed that polynomial model with high degree tends to be less resistant to random errors in the observations.展开更多
The technology of tunnel boring machine(TBM)has been widely applied for underground construction worldwide;however,how to ensure the TBM tunneling process safe and efficient remains a major concern.Advance rate is a k...The technology of tunnel boring machine(TBM)has been widely applied for underground construction worldwide;however,how to ensure the TBM tunneling process safe and efficient remains a major concern.Advance rate is a key parameter of TBM operation and reflects the TBM-ground interaction,for which a reliable prediction helps optimize the TBM performance.Here,we develop a hybrid neural network model,called Attention-ResNet-LSTM,for accurate prediction of the TBM advance rate.A database including geological properties and TBM operational parameters from the Yangtze River Natural Gas Pipeline Project is used to train and test this deep learning model.The evolutionary polynomial regression method is adopted to aid the selection of input parameters.The results of numerical exper-iments show that our Attention-ResNet-LSTM model outperforms other commonly-used intelligent models with a lower root mean square error and a lower mean absolute percentage error.Further,parametric analyses are conducted to explore the effects of the sequence length of historical data and the model architecture on the prediction accuracy.A correlation analysis between the input and output parameters is also implemented to provide guidance for adjusting relevant TBM operational parameters.The performance of our hybrid intelligent model is demonstrated in a case study of TBM tunneling through a complex ground with variable strata.Finally,data collected from the Baimang River Tunnel Project in Shenzhen of China are used to further test the generalization of our model.The results indicate that,compared to the conventional ResNet-LSTM model,our model has a better predictive capability for scenarios with unknown datasets due to its self-adaptive characteristic.展开更多
Passerines moult during various life-cycle stages.Some of these moults involve the retention of a variable quantity of wing and tail feathers.This prompts the question whether these partial moults are just arrested co...Passerines moult during various life-cycle stages.Some of these moults involve the retention of a variable quantity of wing and tail feathers.This prompts the question whether these partial moults are just arrested complete moults or follow different processes.To address it,I investigated whether three relevant features remain constant across partial and complete moults:1) moult sequence(order of activation) within feather tracts(e.g.,consecutive outward moult of primaries) and among tracts(e.g.,starting with marginal coverts,followed by greater coverts second,tertials,etc.);2) dynamics of moult intensity(amount of feathers growing along the moult progress);and 3) protection of wing quills by overlapping fully grown feathers.To study the effect of moult completeness on these three features,I classified moults of 435 individuals from 61 species in 3 groups:i) complete and partial,ii) without and iii) with retention of feathers within tracts.To study the effect of life-cycle stage,I used postbreeding,postjuvenile,and prebreeding moults.I calculated phylogenetically corrected means to establish feather-moult sequence within tracts.I applied linear regression to analyse moult sequence among tracts,and polynomial regression to study the dynamics of moult intensity as moult progresses.Sequence and intensity dynamics of partial moults tended resemble those of the complete moult as moult completeness increased.Sequence within and among feather tracts tended to shift as moult intensity within tracts and number of tracts increased.Activation of primaries advanced in relation to the other feather tracts as number of moulted primaries increased.Tertial quills were protected by the innermost greater covert regardless of moult completeness.These findings suggest that moult is a self-organised process that adjusts to the degree of completeness of plumage renewal.However,protection of quills and differences among species and between postjuvenile-and prebreeding-moult sequences also suggest an active control linked to feather function,including protection and signalling.展开更多
Sequence and intensity are two essential components of bird moult.While the moult sequences of remex tracts are highly homogenous across passerines,other tracts apparently show a high variability.Moreover,order of mou...Sequence and intensity are two essential components of bird moult.While the moult sequences of remex tracts are highly homogenous across passerines,other tracts apparently show a high variability.Moreover,order of moult activation among tracts are insufficiently known.Likewise,dynamics of moult intensity as moult progresses remains poorly known.Here,we provide detailed quantitative description of moult sequence and intensity in the House Sparrow(Passer domesticus).To understand their role,we tested two hypotheses on the:1) protection function of moult sequence,and 2) aerodynamic and physiological constraints on moult intensity.We scored percentage growth of 313 captured sparrows using the mass of the feathers of each tract(also length for remiges)to monitor moult intensity throughout the complete moult progress,which is defined as the fraction of new and growing feathers in a moulting bird relative to the total plumage.Moult sequence was highly variable both within wing coverts and among feather tracts,with moult sequence differing among all birds to some degree.We only found support for the protection function between greater coverts and both tertials and secondaries.Remex-moult intensity conformed to theoretical predictions,therefore lending support to the aerodynamic-constraint hypothesis.Furthermore,remex-moult speed plateaued during the central stages of moult progress.However,overall plumage-moult speed did not fit predictions of the physiological-constraint hypothesis,showing that the remex moult is only constrained by aerodynamics.Our results indicate that aerodynamic loss is not simply the inevitable effect of moult,but that moult is finely regulated to reduce aerodynamic loss.We propose that the moult of the House Sparrow is controlled through sequence and intensity adjustments in order to:1) avoid body and wing growth peaks;2) fulfil the protection function between some key feather tracts;3) reduce detrimental effects on flight ability;4) keep remex sequence fixed;and 5) relax remex replacement to last the whole moult duration.展开更多
Certain literature that constructs a multifactor stock selection model adopted a weighted-scoring approach despite its three shortcomings.First,it cannot effectively identify the connection between the weights of stoc...Certain literature that constructs a multifactor stock selection model adopted a weighted-scoring approach despite its three shortcomings.First,it cannot effectively identify the connection between the weights of stock-picking concepts and portfolio performances.Second,it cannot provide stock-picking concepts’optimal combination of weights.Third,it cannot meet various investor preferences.Thus,this study employs a mixture experimental design to determine the weights of stock-picking concepts,collect portfolio performance data,and construct performance prediction models based on the weights of stock-picking concepts.Furthermore,these performance prediction models and optimization techniques are employed to discover stock-picking concepts’optimal combination of weights that meet investor preferences.The samples consist of stocks listed on the Taiwan stock market.The modeling and testing periods were 1997–2008 and 2009–2015,respectively.Empirical evidence showed(1)that our methodology is robust in predicting performance accurately,(2)that it can identify significant interactions between stock-picking concepts’weights,and(3)that which their optimal combination should be.This combination of weights can form stock portfolios with the best performances that can meet investor preferences.Thus,our methodology can fill the three drawbacks of the classical weighted-scoring approach.展开更多
Louisiana is endowed with forest resources. Forest wastes generated after thinning, land clearing, and logging operations, such as wood debris, tree trimmings, barks, sawdust, wood chips, and black liquor, among other...Louisiana is endowed with forest resources. Forest wastes generated after thinning, land clearing, and logging operations, such as wood debris, tree trimmings, barks, sawdust, wood chips, and black liquor, among others, can serve as potential fuels for energy production in Louisiana. This paper aims to evaluate the potential annual volumes of forest wastes established on detailed and existing data on the forest structure in the rural-urban interface of Louisiana. It also demonstrates the state’s prospects of utilizing forest wastes to produce bio-oils. The data specific to the study was deduced from secondary data sources to obtain the annual average total residue production in Louisiana and estimate the number of logging residues available for procurement for bioenergy production. The total biomass production per year was modeled versus years by polynomial regression curve fitting using Microsoft Excel. Results of the model show that the cumulative annual total biomass production for 2025 and 2030 in Louisiana is projected to be 80000000 Bone Dry Ton (BDT) and 16000000 (BDT) respectively. The findings of the study depict that Louisiana has a massive biomass supply from forest wastes for bioenergy production. Thus, the potential for Louisiana to become an influential player in the production of bio-based products from forest residues is evident. The author recommends that future research can use Geographic Information Systems (GIS) to create maps displaying the potential locations and utilization centers of forest wastes for bioenergy production in the state.展开更多
The impacts of climate change are being felt in Louisiana, in the form of changing weather patterns that have resulted in changes in floods, hurricanes, tornadoes frequencies of occurrence, and magnitudes, among other...The impacts of climate change are being felt in Louisiana, in the form of changing weather patterns that have resulted in changes in floods, hurricanes, tornadoes frequencies of occurrence, and magnitudes, among others resulting in, flooding. The variabilities in rainfall in a drainage basin affect water availability and sustainability. This study analyzed the precipitation data of Southeastern Louisiana, United States, for the period 1990 to 2020. Data used in the study was from, Donaldsonville, Galliano, Lafourche, Gonzales, Ascension, Morgan, New Orleans, Audubon, Plaquemine, and Ponchatoula, Tangipahoa, weather stations. These stations were selected because the differences between each of their highest and lowest average annual rainfall data were greater than 20 inches. To investigate climate patterns and trends for the given weather stations in Southeastern Louisiana, precipitation data were analyzed on annual time scales using data collected from the World Bank Group Climate Change Knowledge Portal for Development Practitioners and Policy Makers and the Applied Climate Information System (ACIS) of the National Weather Service Prediction Center. The data were further aggregated using annual average blocks of 4 years, and linear and polynomial regression was performed to establish trends. The highest and lowest average annual rainfall data for Donaldsonville, Galliano, Lafourche, Gonzales, Ascension, Morgan, New Orleans, Audubon, Plaquemine, and Ponchatoula, Tangipahoa, weather stations were, 75 and 48, 71 and 44, 73.5 and 52.7, 75 and 46.4, 72 and 41.3, 94 and 55.3, Ponchatoula, and 78.6 and 44, respectively. Plaquemine recorded the highest average annual average rainfall while New Orleans, Audubon station recorded the lowest. The projection of the precipitation in 2030 has been carried out to inform scientists and stakeholders about the approximate quantity of rainfall expected and enable them to make their expected impacts on agriculture, economy, etc. The precipitation for 2030 was predicted by extrapolating models for the weather stations. The data used for the modeling was selected based on the data entries most representative. Hence, the coefficient of correlation and the number of data entries were both considered. Extrapolating results for 2030 precipitation in Donaldsonville, Galliano, Gonzales, Morgan, New Orleans, Audubon, and Plaquemine were found to be within the ranges, (85.6 - 86.7), (75.55 - 76.60), (89.7 - 90.67), (99.9 - 100.5), (71.68 - 72.66), and (107.7 - 108.8) inches, respectively. Hence, the average annual precipitations in areas covered by these stations except for Plaquemine station are expected to significantly increase. A restively low increase in average precipitation is expected for Plaquemine station. The increase could impact agriculture negatively or positively depending on the crop’s soil moisture tolerance.展开更多
Predicting the direction of the stock market has always been a huge challenge.Also,the way of forecasting the stock market reduces the risk in the financial market,thus ensuring that brokers can make normal returns.De...Predicting the direction of the stock market has always been a huge challenge.Also,the way of forecasting the stock market reduces the risk in the financial market,thus ensuring that brokers can make normal returns.Despite the complexities of the stock market,the challenge has been increasingly addressed by experts in a variety of disciplines,including economics,statistics,and computer science.The introduction of machine learning,in-depth understanding of the prospects of the financial market,thus doing many experiments to predict the future so that the stock price trend has different degrees of success.In this paper,we propose a method to predict stocks from different industries and markets,as well as trend prediction using traditional machine learning algorithms such as linear regression,polynomial regression and learning techniques in time series prediction using two forms of special types of recursive neural networks:long and short time memory(LSTM)and spoken short-term memory.展开更多
Machine learning algorithms are trained and compared to identify and to characterise the impact on typical aerospace panels of different geometry.Experimental activities are conducted to build a proper impacts’datase...Machine learning algorithms are trained and compared to identify and to characterise the impact on typical aerospace panels of different geometry.Experimental activities are conducted to build a proper impacts’dataset.Polynomial regression algorithm and artificial neural network are applied and optimised to panels without stringer to test their capability to identify the impacts.Subsequently,the algorithms are applied to panels reinforced with stringers that represent a significant increase of complexity in terms of dynamic features of the system to test:the focus is not only on the impact position’s detection but also on the event’s severity.After the identification of the best algorithm,the corresponding machine learning model is deployed on an ARM processor minicomputer,implementing an impact detection system,able to be installed on board an aerial vehicle,making it a smart aircraft equipped with an artificial intelligence decision-making system.展开更多
To date,the accurate prediction of tunnel boring machine(TBM)performance remains a considerable challenge owing to the complex interactions between the TBM and ground.Using evolutionary polynomial regression(EPR)and r...To date,the accurate prediction of tunnel boring machine(TBM)performance remains a considerable challenge owing to the complex interactions between the TBM and ground.Using evolutionary polynomial regression(EPR)and random forest(RF),this study devel-ops two novel prediction models for TBM performance.Both models can predict the TBM penetration rate and field penetration index as outputs with four input parameters:the uniaxial compressive strength,intact rock brittleness index,distance between planes of weakness,and angle between the tunnel axis and planes of weakness(a).First,the performances of both EPR-and RF-based models are examined by comparison with the conventional numerical regression method(i.e.,multivariate linear regression).Subsequently,the performances of the RF-and EPR-based models are further investigated and compared,including the model robustness for unknown datasets,interior relationships between input and output parameters,and variable importance.The results indicate that the RF-based model has greater prediction accuracy,particularly in identifying outliers,whereas the EPR-based model is more convenient to use by field engineers owing to its explicit expression.Both EPR-and RF-based models can accurately identify the relationships between the input and output param-eters.This ensures their excellent generalization ability and high prediction accuracy on unknown datasets.展开更多
The advancement in technologies made the entire manufacturing system,to be operated with more efficient,flexible,user friendly,more productive and cost effective.One such a system to be focused for advancement is plas...The advancement in technologies made the entire manufacturing system,to be operated with more efficient,flexible,user friendly,more productive and cost effective.One such a system to be focused for advancement is plasma cutting system,which has wider industrial applications.There are many researches pursuing at various area of plasma cutting technology,still the automated and optimized parameters value selection is challenging.The work is aimed to eliminate the manual mode of feeding the input parameters for cutting operation.At present,cutting parameters are fed by referring the past cut data information or with the assistance of experienced employers.The cutting process parameters selections will have direct impact on the quality of the material being cut,and life of the consumables.This paper is intended to automate the process parameters selection by developing the mathematical model with existing cutting process parameters database.In this,three different approaches,multiple regression,multiple polynomial regression and AI technique,are selected and analyzed with the mathematical relations developed between the different cutting process parameters.The accuracy and reliability of those methods are detailed.The advantage and disadvantage of those methods for optimal setting conditions are discussed.The appropriate method that can be preferred for automated and optimal settings are elucidated.Finally,the selected technique is checked for accuracy and reliability for the existing cut data.展开更多
Suction assisted installation of caisson foundations in sand relies on the developed seepage around the caisson wall.Seepage is known to produce soil loosening inside the caisson cavity and an overall reduction in soi...Suction assisted installation of caisson foundations in sand relies on the developed seepage around the caisson wall.Seepage is known to produce soil loosening inside the caisson cavity and an overall reduction in soil resistance to caisson penetration.On the other hand,suction must be controlled so that no excessive piping is induced within the sand volume trapped inside the caisson cavity.When it extends over the full embedded length of the caisson wall,piping may lead to the formation of piping channels,which may compromise the established seal between caisson and soil and ultimately cause the installation process to stop.A safe installation process requires a proper design procedure to ensure that a safe suction can be predicted prior to installation.The present paper provides a framework where analytical expressions are obtained for the required suction magnitude,and for the critical suction that causes piping to initiate at the caisson tip.These analytical expressions are derived for a normalized caisson geometry,based on compiled results obtained from finite element analysis of seepage around a caisson wall,at various installation depths.The developed analytical formulation applies independently of caisson dimensions such as diameter,height and wall thickness.Critical suction for piping condition is also obtained under analytical form as a function of normalized penetration depth.The developed formulation can also be easily incorporated into design procedures or used in design codes without a need for a preliminary seepage analysis to be undertaken.The proposed suction predictions for the whole process of caisson installation in sand are validated against field trials reported in the literature.展开更多
Ventilation fans are an important component of any mechanically ventilated building.Poor fan performance could significantly affect the whole building performance metrics.There are several issues such as dirty blades,...Ventilation fans are an important component of any mechanically ventilated building.Poor fan performance could significantly affect the whole building performance metrics.There are several issues such as dirty blades,mechanical wear,aging of fans could impact the fan’s performance.In present work,a novel,indirect and data-driven methodology is introduced to monitor the ventilation fan unit performance.The proposed method is able to perform continuous monitoring of ventilation fan unit in real-time.The real-time performance of 3 Air handling unit(AHU)fans is examined in an academic building.Expected fan performance is modeled with the help of manufacturer data and compared against the real-time performance.Two data-driven models are developed and implemented.The first model is used to compute expected total fan pressure at a given airflow rate while second is a Support Vector Regression(SVR)model,to predict the fan efficiency.The performance monitoring of the ventilation fan unit is determined in terms of expected and actual fan energy consumption.Findings indicated a significant performance gap in three ventilation fan unit in a case building known as OU44,located in city Odense,Denmark.The advantage of this method comprises simplicity,no direct human intervention and scalability to the series of ventilation units.展开更多
文摘In this paper, auxiliary information is used to determine an estimator of finite population total using nonparametric regression under stratified random sampling. To achieve this, a model-based approach is adopted by making use of the local polynomial regression estimation to predict the nonsampled values of the survey variable y. The performance of the proposed estimator is investigated against some design-based and model-based regression estimators. The simulation experiments show that the resulting estimator exhibits good properties. Generally, good confidence intervals are seen for the nonparametric regression estimators, and use of the proposed estimator leads to relatively smaller values of RE compared to other estimators.
文摘Studies on drug combinations are becoming more and more popular in the past few decades, with the development of computer and algorithms. One of the most common methods in optimizing drug combinations is regression of a polynomial model based on certain number of experimental observations. In this paper, we study how to determine the degree of polynomials in different circumstances of drug combination optimization. Using cross-validation, we have found that in most cases, a high degree results in failures of accurate prediction, named overfitting. An anti-noise test has also revealed that polynomial model with high degree tends to be less resistant to random errors in the observations.
基金The research was supported by the National Natural Science Foundation of China(Grant No.52008307)the Shanghai Sci-ence and Technology Innovation Program(Grant No.19DZ1201004)The third author would like to acknowledge the funding by the China Postdoctoral Science Foundation(Grant No.2023M732670).
文摘The technology of tunnel boring machine(TBM)has been widely applied for underground construction worldwide;however,how to ensure the TBM tunneling process safe and efficient remains a major concern.Advance rate is a key parameter of TBM operation and reflects the TBM-ground interaction,for which a reliable prediction helps optimize the TBM performance.Here,we develop a hybrid neural network model,called Attention-ResNet-LSTM,for accurate prediction of the TBM advance rate.A database including geological properties and TBM operational parameters from the Yangtze River Natural Gas Pipeline Project is used to train and test this deep learning model.The evolutionary polynomial regression method is adopted to aid the selection of input parameters.The results of numerical exper-iments show that our Attention-ResNet-LSTM model outperforms other commonly-used intelligent models with a lower root mean square error and a lower mean absolute percentage error.Further,parametric analyses are conducted to explore the effects of the sequence length of historical data and the model architecture on the prediction accuracy.A correlation analysis between the input and output parameters is also implemented to provide guidance for adjusting relevant TBM operational parameters.The performance of our hybrid intelligent model is demonstrated in a case study of TBM tunneling through a complex ground with variable strata.Finally,data collected from the Baimang River Tunnel Project in Shenzhen of China are used to further test the generalization of our model.The results indicate that,compared to the conventional ResNet-LSTM model,our model has a better predictive capability for scenarios with unknown datasets due to its self-adaptive characteristic.
文摘Passerines moult during various life-cycle stages.Some of these moults involve the retention of a variable quantity of wing and tail feathers.This prompts the question whether these partial moults are just arrested complete moults or follow different processes.To address it,I investigated whether three relevant features remain constant across partial and complete moults:1) moult sequence(order of activation) within feather tracts(e.g.,consecutive outward moult of primaries) and among tracts(e.g.,starting with marginal coverts,followed by greater coverts second,tertials,etc.);2) dynamics of moult intensity(amount of feathers growing along the moult progress);and 3) protection of wing quills by overlapping fully grown feathers.To study the effect of moult completeness on these three features,I classified moults of 435 individuals from 61 species in 3 groups:i) complete and partial,ii) without and iii) with retention of feathers within tracts.To study the effect of life-cycle stage,I used postbreeding,postjuvenile,and prebreeding moults.I calculated phylogenetically corrected means to establish feather-moult sequence within tracts.I applied linear regression to analyse moult sequence among tracts,and polynomial regression to study the dynamics of moult intensity as moult progresses.Sequence and intensity dynamics of partial moults tended resemble those of the complete moult as moult completeness increased.Sequence within and among feather tracts tended to shift as moult intensity within tracts and number of tracts increased.Activation of primaries advanced in relation to the other feather tracts as number of moulted primaries increased.Tertial quills were protected by the innermost greater covert regardless of moult completeness.These findings suggest that moult is a self-organised process that adjusts to the degree of completeness of plumage renewal.However,protection of quills and differences among species and between postjuvenile-and prebreeding-moult sequences also suggest an active control linked to feather function,including protection and signalling.
基金the Natural Sciences Museum of Barcelona(PASSERCAT-2 project)to JQ.
文摘Sequence and intensity are two essential components of bird moult.While the moult sequences of remex tracts are highly homogenous across passerines,other tracts apparently show a high variability.Moreover,order of moult activation among tracts are insufficiently known.Likewise,dynamics of moult intensity as moult progresses remains poorly known.Here,we provide detailed quantitative description of moult sequence and intensity in the House Sparrow(Passer domesticus).To understand their role,we tested two hypotheses on the:1) protection function of moult sequence,and 2) aerodynamic and physiological constraints on moult intensity.We scored percentage growth of 313 captured sparrows using the mass of the feathers of each tract(also length for remiges)to monitor moult intensity throughout the complete moult progress,which is defined as the fraction of new and growing feathers in a moulting bird relative to the total plumage.Moult sequence was highly variable both within wing coverts and among feather tracts,with moult sequence differing among all birds to some degree.We only found support for the protection function between greater coverts and both tertials and secondaries.Remex-moult intensity conformed to theoretical predictions,therefore lending support to the aerodynamic-constraint hypothesis.Furthermore,remex-moult speed plateaued during the central stages of moult progress.However,overall plumage-moult speed did not fit predictions of the physiological-constraint hypothesis,showing that the remex moult is only constrained by aerodynamics.Our results indicate that aerodynamic loss is not simply the inevitable effect of moult,but that moult is finely regulated to reduce aerodynamic loss.We propose that the moult of the House Sparrow is controlled through sequence and intensity adjustments in order to:1) avoid body and wing growth peaks;2) fulfil the protection function between some key feather tracts;3) reduce detrimental effects on flight ability;4) keep remex sequence fixed;and 5) relax remex replacement to last the whole moult duration.
文摘Certain literature that constructs a multifactor stock selection model adopted a weighted-scoring approach despite its three shortcomings.First,it cannot effectively identify the connection between the weights of stock-picking concepts and portfolio performances.Second,it cannot provide stock-picking concepts’optimal combination of weights.Third,it cannot meet various investor preferences.Thus,this study employs a mixture experimental design to determine the weights of stock-picking concepts,collect portfolio performance data,and construct performance prediction models based on the weights of stock-picking concepts.Furthermore,these performance prediction models and optimization techniques are employed to discover stock-picking concepts’optimal combination of weights that meet investor preferences.The samples consist of stocks listed on the Taiwan stock market.The modeling and testing periods were 1997–2008 and 2009–2015,respectively.Empirical evidence showed(1)that our methodology is robust in predicting performance accurately,(2)that it can identify significant interactions between stock-picking concepts’weights,and(3)that which their optimal combination should be.This combination of weights can form stock portfolios with the best performances that can meet investor preferences.Thus,our methodology can fill the three drawbacks of the classical weighted-scoring approach.
文摘Louisiana is endowed with forest resources. Forest wastes generated after thinning, land clearing, and logging operations, such as wood debris, tree trimmings, barks, sawdust, wood chips, and black liquor, among others, can serve as potential fuels for energy production in Louisiana. This paper aims to evaluate the potential annual volumes of forest wastes established on detailed and existing data on the forest structure in the rural-urban interface of Louisiana. It also demonstrates the state’s prospects of utilizing forest wastes to produce bio-oils. The data specific to the study was deduced from secondary data sources to obtain the annual average total residue production in Louisiana and estimate the number of logging residues available for procurement for bioenergy production. The total biomass production per year was modeled versus years by polynomial regression curve fitting using Microsoft Excel. Results of the model show that the cumulative annual total biomass production for 2025 and 2030 in Louisiana is projected to be 80000000 Bone Dry Ton (BDT) and 16000000 (BDT) respectively. The findings of the study depict that Louisiana has a massive biomass supply from forest wastes for bioenergy production. Thus, the potential for Louisiana to become an influential player in the production of bio-based products from forest residues is evident. The author recommends that future research can use Geographic Information Systems (GIS) to create maps displaying the potential locations and utilization centers of forest wastes for bioenergy production in the state.
文摘The impacts of climate change are being felt in Louisiana, in the form of changing weather patterns that have resulted in changes in floods, hurricanes, tornadoes frequencies of occurrence, and magnitudes, among others resulting in, flooding. The variabilities in rainfall in a drainage basin affect water availability and sustainability. This study analyzed the precipitation data of Southeastern Louisiana, United States, for the period 1990 to 2020. Data used in the study was from, Donaldsonville, Galliano, Lafourche, Gonzales, Ascension, Morgan, New Orleans, Audubon, Plaquemine, and Ponchatoula, Tangipahoa, weather stations. These stations were selected because the differences between each of their highest and lowest average annual rainfall data were greater than 20 inches. To investigate climate patterns and trends for the given weather stations in Southeastern Louisiana, precipitation data were analyzed on annual time scales using data collected from the World Bank Group Climate Change Knowledge Portal for Development Practitioners and Policy Makers and the Applied Climate Information System (ACIS) of the National Weather Service Prediction Center. The data were further aggregated using annual average blocks of 4 years, and linear and polynomial regression was performed to establish trends. The highest and lowest average annual rainfall data for Donaldsonville, Galliano, Lafourche, Gonzales, Ascension, Morgan, New Orleans, Audubon, Plaquemine, and Ponchatoula, Tangipahoa, weather stations were, 75 and 48, 71 and 44, 73.5 and 52.7, 75 and 46.4, 72 and 41.3, 94 and 55.3, Ponchatoula, and 78.6 and 44, respectively. Plaquemine recorded the highest average annual average rainfall while New Orleans, Audubon station recorded the lowest. The projection of the precipitation in 2030 has been carried out to inform scientists and stakeholders about the approximate quantity of rainfall expected and enable them to make their expected impacts on agriculture, economy, etc. The precipitation for 2030 was predicted by extrapolating models for the weather stations. The data used for the modeling was selected based on the data entries most representative. Hence, the coefficient of correlation and the number of data entries were both considered. Extrapolating results for 2030 precipitation in Donaldsonville, Galliano, Gonzales, Morgan, New Orleans, Audubon, and Plaquemine were found to be within the ranges, (85.6 - 86.7), (75.55 - 76.60), (89.7 - 90.67), (99.9 - 100.5), (71.68 - 72.66), and (107.7 - 108.8) inches, respectively. Hence, the average annual precipitations in areas covered by these stations except for Plaquemine station are expected to significantly increase. A restively low increase in average precipitation is expected for Plaquemine station. The increase could impact agriculture negatively or positively depending on the crop’s soil moisture tolerance.
文摘Predicting the direction of the stock market has always been a huge challenge.Also,the way of forecasting the stock market reduces the risk in the financial market,thus ensuring that brokers can make normal returns.Despite the complexities of the stock market,the challenge has been increasingly addressed by experts in a variety of disciplines,including economics,statistics,and computer science.The introduction of machine learning,in-depth understanding of the prospects of the financial market,thus doing many experiments to predict the future so that the stock price trend has different degrees of success.In this paper,we propose a method to predict stocks from different industries and markets,as well as trend prediction using traditional machine learning algorithms such as linear regression,polynomial regression and learning techniques in time series prediction using two forms of special types of recursive neural networks:long and short time memory(LSTM)and spoken short-term memory.
文摘Machine learning algorithms are trained and compared to identify and to characterise the impact on typical aerospace panels of different geometry.Experimental activities are conducted to build a proper impacts’dataset.Polynomial regression algorithm and artificial neural network are applied and optimised to panels without stringer to test their capability to identify the impacts.Subsequently,the algorithms are applied to panels reinforced with stringers that represent a significant increase of complexity in terms of dynamic features of the system to test:the focus is not only on the impact position’s detection but also on the event’s severity.After the identification of the best algorithm,the corresponding machine learning model is deployed on an ARM processor minicomputer,implementing an impact detection system,able to be installed on board an aerial vehicle,making it a smart aircraft equipped with an artificial intelligence decision-making system.
基金supported by the research project of Zhongtian Construction Group Co.Ltd.(Grant No.ZTCG-GDJTYJS-JSFW-2020002).
文摘To date,the accurate prediction of tunnel boring machine(TBM)performance remains a considerable challenge owing to the complex interactions between the TBM and ground.Using evolutionary polynomial regression(EPR)and random forest(RF),this study devel-ops two novel prediction models for TBM performance.Both models can predict the TBM penetration rate and field penetration index as outputs with four input parameters:the uniaxial compressive strength,intact rock brittleness index,distance between planes of weakness,and angle between the tunnel axis and planes of weakness(a).First,the performances of both EPR-and RF-based models are examined by comparison with the conventional numerical regression method(i.e.,multivariate linear regression).Subsequently,the performances of the RF-and EPR-based models are further investigated and compared,including the model robustness for unknown datasets,interior relationships between input and output parameters,and variable importance.The results indicate that the RF-based model has greater prediction accuracy,particularly in identifying outliers,whereas the EPR-based model is more convenient to use by field engineers owing to its explicit expression.Both EPR-and RF-based models can accurately identify the relationships between the input and output param-eters.This ensures their excellent generalization ability and high prediction accuracy on unknown datasets.
文摘The advancement in technologies made the entire manufacturing system,to be operated with more efficient,flexible,user friendly,more productive and cost effective.One such a system to be focused for advancement is plasma cutting system,which has wider industrial applications.There are many researches pursuing at various area of plasma cutting technology,still the automated and optimized parameters value selection is challenging.The work is aimed to eliminate the manual mode of feeding the input parameters for cutting operation.At present,cutting parameters are fed by referring the past cut data information or with the assistance of experienced employers.The cutting process parameters selections will have direct impact on the quality of the material being cut,and life of the consumables.This paper is intended to automate the process parameters selection by developing the mathematical model with existing cutting process parameters database.In this,three different approaches,multiple regression,multiple polynomial regression and AI technique,are selected and analyzed with the mathematical relations developed between the different cutting process parameters.The accuracy and reliability of those methods are detailed.The advantage and disadvantage of those methods for optimal setting conditions are discussed.The appropriate method that can be preferred for automated and optimal settings are elucidated.Finally,the selected technique is checked for accuracy and reliability for the existing cut data.
文摘Suction assisted installation of caisson foundations in sand relies on the developed seepage around the caisson wall.Seepage is known to produce soil loosening inside the caisson cavity and an overall reduction in soil resistance to caisson penetration.On the other hand,suction must be controlled so that no excessive piping is induced within the sand volume trapped inside the caisson cavity.When it extends over the full embedded length of the caisson wall,piping may lead to the formation of piping channels,which may compromise the established seal between caisson and soil and ultimately cause the installation process to stop.A safe installation process requires a proper design procedure to ensure that a safe suction can be predicted prior to installation.The present paper provides a framework where analytical expressions are obtained for the required suction magnitude,and for the critical suction that causes piping to initiate at the caisson tip.These analytical expressions are derived for a normalized caisson geometry,based on compiled results obtained from finite element analysis of seepage around a caisson wall,at various installation depths.The developed analytical formulation applies independently of caisson dimensions such as diameter,height and wall thickness.Critical suction for piping condition is also obtained under analytical form as a function of normalized penetration depth.The developed formulation can also be easily incorporated into design procedures or used in design codes without a need for a preliminary seepage analysis to be undertaken.The proposed suction predictions for the whole process of caisson installation in sand are validated against field trials reported in the literature.
文摘Ventilation fans are an important component of any mechanically ventilated building.Poor fan performance could significantly affect the whole building performance metrics.There are several issues such as dirty blades,mechanical wear,aging of fans could impact the fan’s performance.In present work,a novel,indirect and data-driven methodology is introduced to monitor the ventilation fan unit performance.The proposed method is able to perform continuous monitoring of ventilation fan unit in real-time.The real-time performance of 3 Air handling unit(AHU)fans is examined in an academic building.Expected fan performance is modeled with the help of manufacturer data and compared against the real-time performance.Two data-driven models are developed and implemented.The first model is used to compute expected total fan pressure at a given airflow rate while second is a Support Vector Regression(SVR)model,to predict the fan efficiency.The performance monitoring of the ventilation fan unit is determined in terms of expected and actual fan energy consumption.Findings indicated a significant performance gap in three ventilation fan unit in a case building known as OU44,located in city Odense,Denmark.The advantage of this method comprises simplicity,no direct human intervention and scalability to the series of ventilation units.
