Novus fossilium index

Arkit: 1 arkintunnukseton lehti, M4.

Novus fossilium index

Arkit: 1 arkintunnukseton lehti, D4.

Novus fossilium index

Arkit: 1 arkintunnukseton lehti, H4.

Index fossilium analysibus chemicis examinatorum

Arkit: 2 arkintunnuksetonta lehteä, B4.

Index fossilium analysibus chemicis examinatorum

Arkit: 1 arkintunnukseton lehti, I4 K1.

Novus fossilium index

Arkit: 1 arkintunnukseton lehti, F4.

Index fossilium analysibus chemicis examinatorum

Arkit: 1 arkintunnukseton lehti, C4.

Novus fossilium index

Arkit: 1 arkintunnukseton lehti, A4.

Novus fossilium index

Arkit: 1 arkintunnukseton lehti, L4.

Index fossilium analysibus chemicis examinatorum

Arkit: 1 arkintunnukseton lehti, E4.

Index fossilium analysibus chemicis examinatorum

Arkit: 1 arkintunnukseton lehti, G4 H2.

Quantitative structure-retention relationships analysis of retention index of essential oils

Genetic algorithm and multiple linear regression (GA-MLR), partial least square (GA-PLS), kernel PLS (GA-KPLS) and Levenberg-Marquardt artificial neural network (L-M ANN) techniques were used to investigate the correlation between retention index (RI) and descriptors for 116 diverse compounds in essential oils of six Stachys species. The correlation coefficient LGO-CV (Q²) between experimental and predicted RI for test set by GA-MLR, GA-PLS, GA-KPLS and L-M ANN was 0.886, 0.912, 0.937 and 0.964, respectively. This is the first research on the QSRR of the essential oil compounds against the RI using the GA-KPLS and L-M ANN.

PREDICTING THE BOILING POINT OF PCDD/Fs BY THE QSPR METHOD BASED ON THE MOLECULAR DISTANCE-EDGE VECTOR INDEX

The quantitative structure property relationship (QSPR) for the boiling point (Tb) of polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans (PCDD/Fs) was investigated. The molecular distance-edge vector (MDEV) index was used as the structural descriptor. The quantitative relationship between the MDEV index and Tb was modeled by using multivariate linear regression (MLR) and artificial neural network (ANN), respectively. Leave-one-out cross validation and external validation were carried out to assess the prediction performance of the models developed. For the MLR method, the prediction root mean square relative error (RMSRE) of leave-one-out cross validation and external validation was 1.77 and 1.23, respectively. For the ANN method, the prediction RMSRE of leave-one-out cross validation and external validation was 1.65 and 1.16, respectively. A quantitative relationship between the MDEV index and Tb of PCDD/Fs was demonstrated. Both MLR and ANN are practicable for modeling this relationship. The MLR model and ANN model developed can be used to predict the Tb of PCDD/Fs. Thus, the Tb of each PCDD/F was predicted by the developed models.

Technological and business challenges of smart grids

Growing recognition of the electricity grid modernization to enable new electricity generation and consumption schemes has found articulation in the vision of the Smart Grid platform. The essence of this vision is an autonomous network with two-way electricity power flows and extensive real-time information between the generation nodes, various electricity-dependent appliances and all points in-between. Three major components of the Smart Grids are distributed intelligence, communication technologies, and automated control systems. The aim of this thesis is to recognize the challenges that Smart Grids are facing, while extinguishing the main driving factors for their introduction. The scope of the thesis also covers possible place of electricity Aggregator Company in the current and future electricity markets. Basic functions of an aggregator and possible revenue sources along with demand response feasibility calculations are reviewed within this thesis.