Test Case Prioritization(TCP)techniques perform better than other regression test optimization techniques including Test Suite Reduction(TSR)and Test Case Selection(TCS).Many TCP techniques are available,and their per...Test Case Prioritization(TCP)techniques perform better than other regression test optimization techniques including Test Suite Reduction(TSR)and Test Case Selection(TCS).Many TCP techniques are available,and their performance is usually measured through a metric Average Percentage of Fault Detection(APFD).This metric is value-neutral because it only works well when all test cases have the same cost,and all faults have the same severity.Using APFD for performance evaluation of test case orders where test cases cost or faults severity varies is prone to produce false results.Therefore,using the right metric for performance evaluation of TCP techniques is very important to get reliable and correct results.In this paper,two value-based TCP techniques have been introduced using Genetic Algorithm(GA)including Value-Cognizant Fault Detection-Based TCP(VCFDB-TCP)and Value-Cognizant Requirements Coverage-Based TCP(VCRCB-TCP).Two novel value-based performance evaluation metrics are also introduced for value-based TCP including Average Percentage of Fault Detection per value(APFDv)and Average Percentage of Requirements Coverage per value(APRCv).Two case studies are performed to validate proposed techniques and performance evaluation metrics.The proposed GA-based techniques outperformed the existing state-of-the-art TCP techniques including Original Order(OO),Reverse Order(REV-O),Random Order(RO),and Greedy algorithm.展开更多
Fungi provide ecological and environmental services to humans, as well as health and nutritional benefits, and are vital to numerousindustries. Fermented food and beverage products from fungi are circulating in the ma...Fungi provide ecological and environmental services to humans, as well as health and nutritional benefits, and are vital to numerousindustries. Fermented food and beverage products from fungi are circulating in the market, generating billions of USD.However, the highest potential monetary value of fungi is their role in blue carbon trading because of their ability to sequesterlarge amounts of carbon in the soil. There are no conclusive estimates available on the global monetary value of fungi, primarilybecause there are limited data for extrapolation. This study outlines the contribution of fungi to the global economy and providesa first attempt at quantifying the global monetary value of fungi. Our estimate of USD 54.57 trillion provides a starting point thatcan be analysed and improved, highlighting the significance of fungi and providing an appreciation of their value. This paperidentifies the different economically valuable products and services provided by fungi. By giving a monetary value to all importantfungal products, services, and industrial applications underscores their significance in biodiversity and conservation. Furthermore,if the value of fungi is well established, they will be considered in future policies for effective ecosystem management.展开更多
Ni-based catalysts supported by γ-Al_2O_3 were prepared for improving the lower heating value( LHV) of biomass gasification fuel gas through methanation. Prior to the performance tests, the physico-chemical propertie...Ni-based catalysts supported by γ-Al_2O_3 were prepared for improving the lower heating value( LHV) of biomass gasification fuel gas through methanation. Prior to the performance tests, the physico-chemical properties of the catalyst samples were characterized by N_2 isothermal adsorption/desorption, X-ray diffraction( XRD) and a scanning electron microscope( SEM). Afterwards, a series of experiments were carried out to investigate the catalytic performance and the results showthat catalysts with 15% and20% Ni loadings have better methanation catalytic effect than those with 5% and 10% Ni loadings in terms of elevating the LHV of biomass gasification fuel gas. M oreover, controllable influential factors such as the reaction temperature, the H_2/CO ratio and the water content occupy an important position in the methanation of biomass gasification fuel gas. 15 Ni/γ-Al_2O_3 and 20 Ni/γ-Al_2O_3 catalysts have a higher CO conversion and CH_4 selectivity at 350 ℃ and the LHV of biomass gasification fuel gas can be largely increased by 34. 3 % at 350 ℃. Higher H_2/CO ratio and a lower water content are more beneficial for improving the LHV of biomass gasification fuel gas when considering the combination of both CO conversion and CH_4 selectivity. This is due to the fact that a higher H_2/CO ratio and lower water content can increase the extent of the methanation reaction.展开更多
Purpose-Conventional diagnostic techniques,on the other hand,may be prone to subjectivity since they depend on assessment of motions that are often subtle to individual eyes and hence hard to classify,potentially resu...Purpose-Conventional diagnostic techniques,on the other hand,may be prone to subjectivity since they depend on assessment of motions that are often subtle to individual eyes and hence hard to classify,potentially resulting in misdiagnosis.Meanwhile,early nonmotor signs of Parkinson’s disease(PD)can be mild and may be due to variety of other conditions.As a result,these signs are usually ignored,making early PD diagnosis difficult.Machine learning approaches for PD classification and healthy controls or individuals with similar medical symptoms have been introduced to solve these problems and to enhance the diagnostic and assessment processes of PD(like,movement disorders or other Parkinsonian syndromes).Design/methodology/approach-Medical observations and evaluation of medical symptoms,including characterization of a wide range of motor indications,are commonly used to diagnose PD.The quantity of the data being processed has grown in the last five years;feature selection has become a prerequisite before any classification.This study introduces a feature selection method based on the score-based artificial fish swarm algorithm(SAFSA)to overcome this issue.Findings-This study adds to the accuracy of PD identification by reducing the amount of chosen vocal features while to use the most recent and largest publicly accessible database.Feature subset selection in PD detection techniques starts by eliminating features that are not relevant or redundant.According to a few objective functions,features subset chosen should provide the best performance.Research limitations/implications-In many situations,this is an Nondeterministic Polynomial Time(NPHard)issue.This method enhances the PD detection rate by selecting the most essential features from the database.To begin,the data set’s dimensionality is reduced using Singular Value Decomposition dimensionality technique.Next,Biogeography-Based Optimization(BBO)for feature selection;the weight value is a vital parameter for finding the best features in PD classification.O展开更多
文摘Test Case Prioritization(TCP)techniques perform better than other regression test optimization techniques including Test Suite Reduction(TSR)and Test Case Selection(TCS).Many TCP techniques are available,and their performance is usually measured through a metric Average Percentage of Fault Detection(APFD).This metric is value-neutral because it only works well when all test cases have the same cost,and all faults have the same severity.Using APFD for performance evaluation of test case orders where test cases cost or faults severity varies is prone to produce false results.Therefore,using the right metric for performance evaluation of TCP techniques is very important to get reliable and correct results.In this paper,two value-based TCP techniques have been introduced using Genetic Algorithm(GA)including Value-Cognizant Fault Detection-Based TCP(VCFDB-TCP)and Value-Cognizant Requirements Coverage-Based TCP(VCRCB-TCP).Two novel value-based performance evaluation metrics are also introduced for value-based TCP including Average Percentage of Fault Detection per value(APFDv)and Average Percentage of Requirements Coverage per value(APRCv).Two case studies are performed to validate proposed techniques and performance evaluation metrics.The proposed GA-based techniques outperformed the existing state-of-the-art TCP techniques including Original Order(OO),Reverse Order(REV-O),Random Order(RO),and Greedy algorithm.
基金the Thailand Science Research and Innovation grant“Macrofungi diversity research from the Lancang-Mekong Watershed and surrounding areas”(Grant No.DBG6280009)the National Research Council of Thailand(NRCT)grant“Total fungal diversity in a given forest area with implications towards species numbers,chemical diversity and biotechnology”(Grant No.N42A650547+1 种基金We thank the Mushroom Research Foundation,Chiang Rai,Thailand for supporting the Ph.D studies of A.G.Niego.Niego would like to acknowledge the Mae Fah Luang University Thesis/Dissertation Support Grant(Reference No.Oh 7702(6)/49)EC-G was supported by the HZI POF IV Cooperativity and Creativity Project Call and CL was funded by a PhD stipend of the Life Science Foundation,Munich.
文摘Fungi provide ecological and environmental services to humans, as well as health and nutritional benefits, and are vital to numerousindustries. Fermented food and beverage products from fungi are circulating in the market, generating billions of USD.However, the highest potential monetary value of fungi is their role in blue carbon trading because of their ability to sequesterlarge amounts of carbon in the soil. There are no conclusive estimates available on the global monetary value of fungi, primarilybecause there are limited data for extrapolation. This study outlines the contribution of fungi to the global economy and providesa first attempt at quantifying the global monetary value of fungi. Our estimate of USD 54.57 trillion provides a starting point thatcan be analysed and improved, highlighting the significance of fungi and providing an appreciation of their value. This paperidentifies the different economically valuable products and services provided by fungi. By giving a monetary value to all importantfungal products, services, and industrial applications underscores their significance in biodiversity and conservation. Furthermore,if the value of fungi is well established, they will be considered in future policies for effective ecosystem management.
基金The International S&T Cooperation Program of China(No.2014DFE70150)
文摘Ni-based catalysts supported by γ-Al_2O_3 were prepared for improving the lower heating value( LHV) of biomass gasification fuel gas through methanation. Prior to the performance tests, the physico-chemical properties of the catalyst samples were characterized by N_2 isothermal adsorption/desorption, X-ray diffraction( XRD) and a scanning electron microscope( SEM). Afterwards, a series of experiments were carried out to investigate the catalytic performance and the results showthat catalysts with 15% and20% Ni loadings have better methanation catalytic effect than those with 5% and 10% Ni loadings in terms of elevating the LHV of biomass gasification fuel gas. M oreover, controllable influential factors such as the reaction temperature, the H_2/CO ratio and the water content occupy an important position in the methanation of biomass gasification fuel gas. 15 Ni/γ-Al_2O_3 and 20 Ni/γ-Al_2O_3 catalysts have a higher CO conversion and CH_4 selectivity at 350 ℃ and the LHV of biomass gasification fuel gas can be largely increased by 34. 3 % at 350 ℃. Higher H_2/CO ratio and a lower water content are more beneficial for improving the LHV of biomass gasification fuel gas when considering the combination of both CO conversion and CH_4 selectivity. This is due to the fact that a higher H_2/CO ratio and lower water content can increase the extent of the methanation reaction.
文摘Purpose-Conventional diagnostic techniques,on the other hand,may be prone to subjectivity since they depend on assessment of motions that are often subtle to individual eyes and hence hard to classify,potentially resulting in misdiagnosis.Meanwhile,early nonmotor signs of Parkinson’s disease(PD)can be mild and may be due to variety of other conditions.As a result,these signs are usually ignored,making early PD diagnosis difficult.Machine learning approaches for PD classification and healthy controls or individuals with similar medical symptoms have been introduced to solve these problems and to enhance the diagnostic and assessment processes of PD(like,movement disorders or other Parkinsonian syndromes).Design/methodology/approach-Medical observations and evaluation of medical symptoms,including characterization of a wide range of motor indications,are commonly used to diagnose PD.The quantity of the data being processed has grown in the last five years;feature selection has become a prerequisite before any classification.This study introduces a feature selection method based on the score-based artificial fish swarm algorithm(SAFSA)to overcome this issue.Findings-This study adds to the accuracy of PD identification by reducing the amount of chosen vocal features while to use the most recent and largest publicly accessible database.Feature subset selection in PD detection techniques starts by eliminating features that are not relevant or redundant.According to a few objective functions,features subset chosen should provide the best performance.Research limitations/implications-In many situations,this is an Nondeterministic Polynomial Time(NPHard)issue.This method enhances the PD detection rate by selecting the most essential features from the database.To begin,the data set’s dimensionality is reduced using Singular Value Decomposition dimensionality technique.Next,Biogeography-Based Optimization(BBO)for feature selection;the weight value is a vital parameter for finding the best features in PD classification.O
