This paper introduces fuzzy N-bipolar soft(FN-BS)sets,a novel mathematical framework designed to enhance multi-criteria decision-making(MCDM)processes under uncertainty.The study addresses a significant limitation in ...This paper introduces fuzzy N-bipolar soft(FN-BS)sets,a novel mathematical framework designed to enhance multi-criteria decision-making(MCDM)processes under uncertainty.The study addresses a significant limitation in existing models by unifying fuzzy logic,the consideration of bipolarity,and the ability to evaluate attributes on a multinary scale.The specific contributions of the FN-BS framework include:(1)a formal definition and settheoretic foundation,(2)the development of two innovative algorithms for solving decision-making(DM)problems,and(3)a comparative analysis demonstrating its superiority over established models.The proposed framework is applied to a real-world case study on selecting vaccination programs across multiple countries,showcasing consistent DM outcomes and exceptional adaptability to complex and uncertain scenarios.These results position FN-BS sets as a versatile and powerful tool for addressing dynamic DM challenges.展开更多
Urban transportation planning involves evaluating multiple conflicting criteria such as accessibility,cost-effectiveness,and environmental impact,often under uncertainty and incomplete information.These complex decisi...Urban transportation planning involves evaluating multiple conflicting criteria such as accessibility,cost-effectiveness,and environmental impact,often under uncertainty and incomplete information.These complex decisions require input from various stakeholders,including planners,policymakers,engineers,and community representatives,whose opinions may differ or contradict.Traditional decision-making approaches struggle to effectively handle such bipolar and multivalued expert evaluations.To address these challenges,we propose a novel decisionmaking framework based on Pythagorean fuzzy N-bipolar soft expert sets.This model allows experts to express both positive and negative opinions on a multinary scale,capturing nuanced judgments with higher accuracy.It introduces algebraic operations and a structured aggregation algorithm to systematically integrate and resolve conflicting expert inputs.Applied to a real-world case study,the framework evaluated five urban transport strategies based on key criteria,producing final scores as follows:improving public transit(−0.70),optimizing traffic signal timing(1.86),enhancing pedestrian infrastructure(3.10),expanding bike lanes(0.59),and implementing congestion pricing(0.77).The results clearly identify enhancing pedestrian infrastructure as the most suitable option,having obtained the highest final score of 3.10.Comparative analysis demonstrates the framework’s superior capability in modeling expert consensus,managing uncertainty,and supporting transparent multi-criteria group decision-making.展开更多
目的探索伴有焦虑症状双相抑郁患者认知功能与N-甲基-D-天冬氨酸受体2B亚基(glutamate iono-tropic receptor NMDA type subunit 2B,GRIN2B)基因启动子区各CpG位点甲基化水平的相关性。方法根据汉密尔顿焦虑量表(14-item Hamilton anxie...目的探索伴有焦虑症状双相抑郁患者认知功能与N-甲基-D-天冬氨酸受体2B亚基(glutamate iono-tropic receptor NMDA type subunit 2B,GRIN2B)基因启动子区各CpG位点甲基化水平的相关性。方法根据汉密尔顿焦虑量表(14-item Hamilton anxiety rating scale,HAMA)评分将31例双相抑郁患者分为焦虑组15例和非焦虑组16例,同期选取16名健康对照。采用蒙特利尔认知评估量表(Montreal cognitive assessment,MoCA)、数值广度测验(digital span test,DST)、连线测试A部分(trail making test A,TMT-A)、斯特鲁普色词测验(Stroop color and word test,SCWT)评估3组总体认知功能、注意力及执行控制、信息处理速度、执行功能等认知功能维度,采用Massarray质谱法检测所有受试者外周血GRIN2B基因启动子区各CpG位点的DNA甲基化水平。结果3组GRIN2B基因启动子区DNA甲基化水平差异性位点为CpG3、CpG5、CpG7、CpG10、CpG12(P<0.05),其中,焦虑组CpG12甲基化水平低于非焦虑组(36.23%±16.41%vs.50.20%±19.79%,P<0.05)。偏相关分析显示,焦虑组患者中较差的命名能力与GRIN2B基因CpG4低甲基化水平相关(r=0.670,P=0.034),较差的执行功能与CpG6低甲基化水平相关(r=0.926,P<0.001),较差的注意力与GRIN2B基因CpG8高甲基化水平相关(r=-0.810,P=0.025),较差的言语记忆与CpG9高甲基化水平相关(r=-0.810,P<0.001),较差的抽象能力与CpG10高甲基化水平相关(r=-0.756,P=0.011)。结论GRIN2B基因启动子区DNA甲基化水平与伴有焦虑症状双相抑郁患者认知功能损害可能有关联,与双相抑郁患者焦虑症状的产生也可能有关联。展开更多
文摘This paper introduces fuzzy N-bipolar soft(FN-BS)sets,a novel mathematical framework designed to enhance multi-criteria decision-making(MCDM)processes under uncertainty.The study addresses a significant limitation in existing models by unifying fuzzy logic,the consideration of bipolarity,and the ability to evaluate attributes on a multinary scale.The specific contributions of the FN-BS framework include:(1)a formal definition and settheoretic foundation,(2)the development of two innovative algorithms for solving decision-making(DM)problems,and(3)a comparative analysis demonstrating its superiority over established models.The proposed framework is applied to a real-world case study on selecting vaccination programs across multiple countries,showcasing consistent DM outcomes and exceptional adaptability to complex and uncertain scenarios.These results position FN-BS sets as a versatile and powerful tool for addressing dynamic DM challenges.
文摘Urban transportation planning involves evaluating multiple conflicting criteria such as accessibility,cost-effectiveness,and environmental impact,often under uncertainty and incomplete information.These complex decisions require input from various stakeholders,including planners,policymakers,engineers,and community representatives,whose opinions may differ or contradict.Traditional decision-making approaches struggle to effectively handle such bipolar and multivalued expert evaluations.To address these challenges,we propose a novel decisionmaking framework based on Pythagorean fuzzy N-bipolar soft expert sets.This model allows experts to express both positive and negative opinions on a multinary scale,capturing nuanced judgments with higher accuracy.It introduces algebraic operations and a structured aggregation algorithm to systematically integrate and resolve conflicting expert inputs.Applied to a real-world case study,the framework evaluated five urban transport strategies based on key criteria,producing final scores as follows:improving public transit(−0.70),optimizing traffic signal timing(1.86),enhancing pedestrian infrastructure(3.10),expanding bike lanes(0.59),and implementing congestion pricing(0.77).The results clearly identify enhancing pedestrian infrastructure as the most suitable option,having obtained the highest final score of 3.10.Comparative analysis demonstrates the framework’s superior capability in modeling expert consensus,managing uncertainty,and supporting transparent multi-criteria group decision-making.
文摘目的探索伴有焦虑症状双相抑郁患者认知功能与N-甲基-D-天冬氨酸受体2B亚基(glutamate iono-tropic receptor NMDA type subunit 2B,GRIN2B)基因启动子区各CpG位点甲基化水平的相关性。方法根据汉密尔顿焦虑量表(14-item Hamilton anxiety rating scale,HAMA)评分将31例双相抑郁患者分为焦虑组15例和非焦虑组16例,同期选取16名健康对照。采用蒙特利尔认知评估量表(Montreal cognitive assessment,MoCA)、数值广度测验(digital span test,DST)、连线测试A部分(trail making test A,TMT-A)、斯特鲁普色词测验(Stroop color and word test,SCWT)评估3组总体认知功能、注意力及执行控制、信息处理速度、执行功能等认知功能维度,采用Massarray质谱法检测所有受试者外周血GRIN2B基因启动子区各CpG位点的DNA甲基化水平。结果3组GRIN2B基因启动子区DNA甲基化水平差异性位点为CpG3、CpG5、CpG7、CpG10、CpG12(P<0.05),其中,焦虑组CpG12甲基化水平低于非焦虑组(36.23%±16.41%vs.50.20%±19.79%,P<0.05)。偏相关分析显示,焦虑组患者中较差的命名能力与GRIN2B基因CpG4低甲基化水平相关(r=0.670,P=0.034),较差的执行功能与CpG6低甲基化水平相关(r=0.926,P<0.001),较差的注意力与GRIN2B基因CpG8高甲基化水平相关(r=-0.810,P=0.025),较差的言语记忆与CpG9高甲基化水平相关(r=-0.810,P<0.001),较差的抽象能力与CpG10高甲基化水平相关(r=-0.756,P=0.011)。结论GRIN2B基因启动子区DNA甲基化水平与伴有焦虑症状双相抑郁患者认知功能损害可能有关联,与双相抑郁患者焦虑症状的产生也可能有关联。
