为进一步准确核算与深入分析民航碳排放,基于2007—2016年全国客货运航班运行数据对中国航空器起飞着陆(landing and take-off,LTO)阶段和爬升巡航下降(climb cruise and descent,CCD)阶段的碳排放量进行测算,并通过航线分段将航班CCD...为进一步准确核算与深入分析民航碳排放,基于2007—2016年全国客货运航班运行数据对中国航空器起飞着陆(landing and take-off,LTO)阶段和爬升巡航下降(climb cruise and descent,CCD)阶段的碳排放量进行测算,并通过航线分段将航班CCD阶段碳排放分配至相关省份.最后分别从全国、区域,以及省域三个层次对民航碳排放核算结果及演化特征进行分析.结果表明:全国航空器碳排放总量呈快速、持续增长趋势;东部地区排放占全国排放总量比例最高,其次为中部、西部,但地区间排放差异有所缩小;CCD阶段碳排放是航空器运行过程中的主要排放;省域碳排放量不仅与省内航空业发达程度有关,还与省份地理位置、省域面积相关;多数省份被动排放对省份总碳排放量的贡献高于主动排放.展开更多
In this study,our aim is to address the problem of gene selection by proposing a hybrid bio-inspired evolutionary algorithm that combines Grey Wolf Optimization(GWO)with Harris Hawks Optimization(HHO)for feature selec...In this study,our aim is to address the problem of gene selection by proposing a hybrid bio-inspired evolutionary algorithm that combines Grey Wolf Optimization(GWO)with Harris Hawks Optimization(HHO)for feature selection.Themotivation for utilizingGWOandHHOstems fromtheir bio-inspired nature and their demonstrated success in optimization problems.We aimto leverage the strengths of these algorithms to enhance the effectiveness of feature selection in microarray-based cancer classification.We selected leave-one-out cross-validation(LOOCV)to evaluate the performance of both two widely used classifiers,k-nearest neighbors(KNN)and support vector machine(SVM),on high-dimensional cancer microarray data.The proposed method is extensively tested on six publicly available cancer microarray datasets,and a comprehensive comparison with recently published methods is conducted.Our hybrid algorithm demonstrates its effectiveness in improving classification performance,Surpassing alternative approaches in terms of precision.The outcomes confirm the capability of our method to substantially improve both the precision and efficiency of cancer classification,thereby advancing the development ofmore efficient treatment strategies.The proposed hybridmethod offers a promising solution to the gene selection problem in microarray-based cancer classification.It improves the accuracy and efficiency of cancer diagnosis and treatment,and its superior performance compared to other methods highlights its potential applicability in realworld cancer classification tasks.By harnessing the complementary search mechanisms of GWO and HHO,we leverage their bio-inspired behavior to identify informative genes relevant to cancer diagnosis and treatment.展开更多
文摘为进一步准确核算与深入分析民航碳排放,基于2007—2016年全国客货运航班运行数据对中国航空器起飞着陆(landing and take-off,LTO)阶段和爬升巡航下降(climb cruise and descent,CCD)阶段的碳排放量进行测算,并通过航线分段将航班CCD阶段碳排放分配至相关省份.最后分别从全国、区域,以及省域三个层次对民航碳排放核算结果及演化特征进行分析.结果表明:全国航空器碳排放总量呈快速、持续增长趋势;东部地区排放占全国排放总量比例最高,其次为中部、西部,但地区间排放差异有所缩小;CCD阶段碳排放是航空器运行过程中的主要排放;省域碳排放量不仅与省内航空业发达程度有关,还与省份地理位置、省域面积相关;多数省份被动排放对省份总碳排放量的贡献高于主动排放.
基金the Deputyship for Research and Innovation,“Ministry of Education”in Saudi Arabia for funding this research(IFKSUOR3-014-3).
文摘In this study,our aim is to address the problem of gene selection by proposing a hybrid bio-inspired evolutionary algorithm that combines Grey Wolf Optimization(GWO)with Harris Hawks Optimization(HHO)for feature selection.Themotivation for utilizingGWOandHHOstems fromtheir bio-inspired nature and their demonstrated success in optimization problems.We aimto leverage the strengths of these algorithms to enhance the effectiveness of feature selection in microarray-based cancer classification.We selected leave-one-out cross-validation(LOOCV)to evaluate the performance of both two widely used classifiers,k-nearest neighbors(KNN)and support vector machine(SVM),on high-dimensional cancer microarray data.The proposed method is extensively tested on six publicly available cancer microarray datasets,and a comprehensive comparison with recently published methods is conducted.Our hybrid algorithm demonstrates its effectiveness in improving classification performance,Surpassing alternative approaches in terms of precision.The outcomes confirm the capability of our method to substantially improve both the precision and efficiency of cancer classification,thereby advancing the development ofmore efficient treatment strategies.The proposed hybridmethod offers a promising solution to the gene selection problem in microarray-based cancer classification.It improves the accuracy and efficiency of cancer diagnosis and treatment,and its superior performance compared to other methods highlights its potential applicability in realworld cancer classification tasks.By harnessing the complementary search mechanisms of GWO and HHO,we leverage their bio-inspired behavior to identify informative genes relevant to cancer diagnosis and treatment.
