This paper present a simulation study of an evolutionary algorithms, Particle Swarm Optimization PSO algorithm to optimize likelihood function of ARMA（1, 1） model, where maximizing likelihood function is equivalent ...This paper present a simulation study of an evolutionary algorithms, Particle Swarm Optimization PSO algorithm to optimize likelihood function of ARMA（1, 1） model, where maximizing likelihood function is equivalent to maximizing its logarithm, so the objective function ＇obj.fun＇ is maximizing log-likelihood function. Monte Carlo method adapted for implementing and designing the experiments of this simulation. This study including a comparison among three versions of PSO algorithm “Constriction coefficient CCPSO, Inertia weight IWPSO, and Fully Informed FIPSO”, the experiments designed by setting different values of model parameters al, bs sample size n, moreover the parameters of PSO algorithms. MSE used as test statistic to measure the efficiency PSO to estimate model. The results show the ability of PSO to estimate ARMA＇ s parameters, and the minimum values of MSE getting for COPSO.展开更多
Atmospheric models are physical equations based on the ideal gas law. Applied to the atmosphere, this law yields equations for water, vapor (gas), ice, air, humidity, dryness, fire, and heat, thus defining the model o...Atmospheric models are physical equations based on the ideal gas law. Applied to the atmosphere, this law yields equations for water, vapor (gas), ice, air, humidity, dryness, fire, and heat, thus defining the model of key atmospheric parameters. The distribution of these parameters across the entire planet Earth is the origin of the formation of the climatic cycle, which is a normal climatic variation. To do this, the Earth is divided into eight (8) parts according to the number of key parameters to be defined in a physical representation of the model. Following this distribution, numerical models calculate the constants for the formation of water, vapor, ice, dryness, thermal energy (fire), heat, air, and humidity. These models vary in complexity depending on the indirect trigonometric direction and simplicity in the sum of neighboring models. Note that the constants obtained from the equations yield 275.156˚K (2.006˚C) for water, 273.1596˚K (0.00963˚C) for vapor, 273.1633˚K (0.0133˚C) for ice, 0.00365 in/s for atmospheric dryness, 1.996 in<sup>2</sup>/s for humidity, 2.993 in<sup>2</sup>/s for air, 1 J for thermal energy of fire, and 0.9963 J for heat. In summary, this study aims to define the main parameters and natural phenomena contributing to the modification of planetary climate. .展开更多
目的研究以专科护士为主导的“1+1+X”协同管理模式对稳定型心绞痛患者病情、自我管理能力的影响。方法方便选取2021年3月—2023年3月聊城市第二人民医院心血管内科收治的86例稳定型心绞痛患者为研究对象,根据不同护理方法分为常规组和...目的研究以专科护士为主导的“1+1+X”协同管理模式对稳定型心绞痛患者病情、自我管理能力的影响。方法方便选取2021年3月—2023年3月聊城市第二人民医院心血管内科收治的86例稳定型心绞痛患者为研究对象,根据不同护理方法分为常规组和协同管理组,各43例。常规组采用常规护理,协同管理组采用以专科护士为主导的“1+1+X”协同管理模式护理,两组均持续护理1个月。观察对比两组患者护理前后生活质量[健康调查简表(MOS Item Short Form Health Survey,SF-36)]、焦虑抑郁心理状况、自我管理能力[冠心病自我管理行为量表(Coronary Artery Disease Self-management Scale,CSMS)]。结果护理后,协同管理组SF-36量表评分高于常规组,差异有统计学意义(P<0.05);协同管理组焦虑自评量表(38.18±3.52)分、抑郁自评量表(39.21±3.24)分均优于常规组的(43.23±3.61)分、(45.03±3.69)分,差异有统计学意义(t=6.568、7.772,P均<0.05);协同管理组CSMS评分高于常规组,差异有统计学意义(P均<0.05)。结论以专科护士为主导的“1+1+X”协同管理模式应用于稳定型心绞痛患者护理可有效提升生活质量,改善不良心理状态,提高自我管理能力。展开更多
目的探讨自身免疫性甲状腺炎伴发抑郁症动物模型的制备与评价,并基于NOD样受体蛋白-3(NOD-like receptor protein 3,NLRP3)/含半胱氨酸的天冬氨酸蛋白水解酶-1(cysteinyl aspartate specific proteinase-1,Caspase-1)/消皮素D(gasdermin...目的探讨自身免疫性甲状腺炎伴发抑郁症动物模型的制备与评价,并基于NOD样受体蛋白-3(NOD-like receptor protein 3,NLRP3)/含半胱氨酸的天冬氨酸蛋白水解酶-1(cysteinyl aspartate specific proteinase-1,Caspase-1)/消皮素D(gasdermin D,GSDMD)通路加以验证。方法32只NOD.H-2H4小鼠随机分为正常组(N组)、抑郁组(DP组)、自身免疫性甲状腺炎伴抑郁症组(AIT+DP组)、自身免疫性甲状腺炎组(AIT组),每组8只。N组正常饲养,DP组采取5周慢性不可预知温和刺激(chronic unpredictable mild stress,CUMS),AIT组予0.05%碘化钠水溶液建立自身免疫性甲状腺炎模型,AIT+DP组在建立AIT动物模型基础上施加5周CUMS建立AIT+DP动物模型。通过观测小鼠甲状腺组织结构及淋巴细胞浸润情况和血清甲状腺过氧化物酶抗体(thyroid peroxidase antibody,TPOAb)和甲状腺球蛋白抗体(anti-thyroid autoantibodies,TGAb)水平评价小鼠自身免疫性甲状腺炎模型是否制备成功;通过测定体重、糖水偏好率、旷场行为学(中央象限时间、中央象限比例、站立次数、排便次数、毛发梳理时间),大脑皮质、海马病理变化及大脑皮质小胶质细胞焦亡相关蛋白水平评价小鼠抑郁状态。模型小鼠同时符合上述自身免疫性甲状腺炎与抑郁症相关指标检测,则表明AIT+DP动物模型制备成功。结果与N组比较,AIT组与AIT+DP组血清TGAb、TPOAb水平显著增加(P<0.01),甲状腺可见大量炎细胞浸润,DP组与AIT+DP组小鼠中央象限时间、中央象限比例、站立次数、排便次数、毛发梳理时间有不同程度降低,大脑皮质神经胶质细胞增多,神经元细胞减少,伴有部分细胞核萎缩,NLRP3、IL-1β、Caspase-1、GSDMD-N蛋白表达水平显著上调,AIT+DP组尤为明显(P<0.01)。结论0.05%碘化钠水溶液与CUMS可较好地模拟AIT+DP模型动物外在表现与内在指标变化,可为AIT+DP疾病的研究提供动物模型参考。展开更多
文摘This paper present a simulation study of an evolutionary algorithms, Particle Swarm Optimization PSO algorithm to optimize likelihood function of ARMA（1, 1） model, where maximizing likelihood function is equivalent to maximizing its logarithm, so the objective function ＇obj.fun＇ is maximizing log-likelihood function. Monte Carlo method adapted for implementing and designing the experiments of this simulation. This study including a comparison among three versions of PSO algorithm “Constriction coefficient CCPSO, Inertia weight IWPSO, and Fully Informed FIPSO”, the experiments designed by setting different values of model parameters al, bs sample size n, moreover the parameters of PSO algorithms. MSE used as test statistic to measure the efficiency PSO to estimate model. The results show the ability of PSO to estimate ARMA＇ s parameters, and the minimum values of MSE getting for COPSO.
文摘Atmospheric models are physical equations based on the ideal gas law. Applied to the atmosphere, this law yields equations for water, vapor (gas), ice, air, humidity, dryness, fire, and heat, thus defining the model of key atmospheric parameters. The distribution of these parameters across the entire planet Earth is the origin of the formation of the climatic cycle, which is a normal climatic variation. To do this, the Earth is divided into eight (8) parts according to the number of key parameters to be defined in a physical representation of the model. Following this distribution, numerical models calculate the constants for the formation of water, vapor, ice, dryness, thermal energy (fire), heat, air, and humidity. These models vary in complexity depending on the indirect trigonometric direction and simplicity in the sum of neighboring models. Note that the constants obtained from the equations yield 275.156˚K (2.006˚C) for water, 273.1596˚K (0.00963˚C) for vapor, 273.1633˚K (0.0133˚C) for ice, 0.00365 in/s for atmospheric dryness, 1.996 in<sup>2</sup>/s for humidity, 2.993 in<sup>2</sup>/s for air, 1 J for thermal energy of fire, and 0.9963 J for heat. In summary, this study aims to define the main parameters and natural phenomena contributing to the modification of planetary climate. .
文摘目的研究以专科护士为主导的“1+1+X”协同管理模式对稳定型心绞痛患者病情、自我管理能力的影响。方法方便选取2021年3月—2023年3月聊城市第二人民医院心血管内科收治的86例稳定型心绞痛患者为研究对象,根据不同护理方法分为常规组和协同管理组,各43例。常规组采用常规护理,协同管理组采用以专科护士为主导的“1+1+X”协同管理模式护理,两组均持续护理1个月。观察对比两组患者护理前后生活质量[健康调查简表(MOS Item Short Form Health Survey,SF-36)]、焦虑抑郁心理状况、自我管理能力[冠心病自我管理行为量表(Coronary Artery Disease Self-management Scale,CSMS)]。结果护理后,协同管理组SF-36量表评分高于常规组,差异有统计学意义(P<0.05);协同管理组焦虑自评量表(38.18±3.52)分、抑郁自评量表(39.21±3.24)分均优于常规组的(43.23±3.61)分、(45.03±3.69)分,差异有统计学意义(t=6.568、7.772,P均<0.05);协同管理组CSMS评分高于常规组,差异有统计学意义(P均<0.05)。结论以专科护士为主导的“1+1+X”协同管理模式应用于稳定型心绞痛患者护理可有效提升生活质量,改善不良心理状态,提高自我管理能力。
