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"This is one of the first texts in which electro-diffusion of ions in its different aspects is considered as a unified subject. As a subject it is relevant to understanding the behavior of apparently different physical objects such as electrolyte solution, ion-exchangers, ion-selective membranes, and semiconductors. This text treats a selection of topics in electro-diffusion of ions in an aqueous medium --- a nonlinear transport process whose essence is diffusion of ions combined with their migration in a selfconsistent electric field. Electro-Diffusion of Ions is intended to enhance the understanding of particular electro-diffusional transport phenomena and to spark the interest of the mathematical community in the corresponding area of applications and development of a unified scientific vision. Contains some previously unpublished results for electro-diffusional problems."

"Metal ions in the brain are a necessity as well as a poison. The presence of metal ions in the active sites of biological catalysts or metalloproteins and in the biological functioning of nucleic acids is very well documented and they are required for brain activity. On the other hand, metals are very effective in generating oxidative stress. This effect does not only play a role in immunology but also is the root of practically all neurodegenerative disorders by inducing disease via the death of neurons. Managing metal ions in the brain could therefore be an important strategy in the search for therapeutic agents used in the treatment of neurodegenerative diseases. This new title gives an overview to key topics in the area of metal ions in the brain. It focuses on the role of metal ions in neurological systems by describing their advantageous functions as well as their poisonous features. It is therefore of interest for scientists in biochemistry and biophysics, physiology, toxicology as well as for physicians focused on this topic."

"Regresi Poisson adalah model regresi umum yang digunakan untuk menghitung data dengan equidispersion. Namun, perhitungan data tidak selalu memenuhi asumsi ini. Hitungan data tidak terpenuhi asumsi ini ketika overdispersi muncul. Overdispersion adalah suatu kondisi dimana varians lebih besar dari rata-rata. Dalam data verdispersi karena kelebihan nol dan tambahan penyebaran berlebihan dalam nilai-nilai positif, salah satu model alternatif yang dapat digunakan adalah rintangan model binomial negatif. Rintangan model binomial negatif terdiri dari model dua bagian model biner dan model binomial negatif terpotong nol. Tugas akhir ini akan membahas tentang model rintangan konstruksi binomial negatif dan metode Bayesian untuk memperkirakan parameter dalam model binomial negatif rintangan. Algoritma metode Bayesian digunakan dalam proyek ini adalah Markov Chain Monte Carlo-Gibbs Sampling (MCMC-GS). Rintangan negatif model binomial akan diterapkan pada data Parkinson dari Parkinsons Growth Markers Database inisiatif (PPMI). Model diterapkan untuk menemukan faktor risiko dari komplikasi motorik kejadian dan frekuensinya berdasarkan Gerakan Disorder Society-Unified Parkinson Data Skala Timbangan Penyakit (MDS-UPDRS). Variabel signifikan untuk model bagian satu adalah skor total MDS-UPDRS Bagian III dan variabel total skor MDS-UPDRS Bagian II dan III untuk model bagian dua. Hasil lain yang diperoleh dari aplikasi data ini adalah parameter estimasi konvergen.

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Poisson regression is a general regression model used to calculate data with equidispersion. However, data calculations do not always fit this consideration. Count data not met Suppose this overdispersion compilation appears. Overdispersion is a place where variants are greater than average. In data verdispersion due to zero excess and additional excessive spread in positive values, one alternative model that can be used is the constraints of the negative binomial model. The negative binomial obstacle model consists of a two-part binary model and the negative binomial zero-truncated model. This final project will discuss about the negative binomial constraint construction model and the Bayesian method for estimating parameters in the negative binomial resistance model. The Bayesian method algorithm is used in this project is the Markov Monte Carlo-Gibbs Sampling Chain (MCMC-GS). The negative barriers of the binomial model will be applied to Parkinsons data from Parkinsons Growth Markers Database Implementation (PPMI). The model applied to find risk factors for motor complications of events and their frequency is based on the Disorder-Unified Parkinsons Movement Disease Scale Data (MDS-UPDRS). The significant variable for the part one model is MDS-UPDRS Part III total score and MDS-UPDRS Part II and III total variable variables for the part two model. The results obtained from the application of this data are convergent estimation parameters."

"The volume concentrates on modern developments encompassing topics in the wide range from G-quadruplexes via DNAzymes, catalysis at the DNA scaffold, and metal-mediated base pairs to peptide nucleic acids (PNAs) being thus of relevance, e.g., for chemistry and nanotechnology but also for molecular biology and (genetic) diagnostics."

"The first study uses tunable vacuum-ultraviolet radiation from a synchrotron to identify negative ions from twenty four photoexcited polyatomic molecules in the gas phase. From these experiments, Matthew collects a vast amount of data and summarises and reviews ion-pair formation from polyatomic molecules. The second study is on selected ion flow tube mass spectrometry. Matthew investigates the reactions of cations and anions with ethene, monofluoroethene, 1,1-difluoroethene and tetrafluoroethene. In this study Matthew tries to explain why certain products are formed preferentially over other products at a microscopic level of understanding. "

"Distribusi Negative Binomial-Generalized Exponential (NB-GE) merupakan distribusi yang mampu memodelkan data overdispersi dengan extreme excess zeros yaitu lebih dari 80% angka nol dalam data. Distribusi Negative Binomial-Generalized Exponential (NB-GE) merupakan distribusi campuran hasil dari mixing antara distribusi Negative Binomial (NB) dengan distribusi Generalized Exponential (GE). Pembentukan distribusi Negative Binomial-Generalized Exponential (NB-GE) serta karakteristik-karakteristik distribusi Negative Binomial-Generalized Exponential (NB-GE) seperti fungsi kepadatan peluang, momen ke-, mean, variansi, koefisien skewness dan koefisien kurtosis dibahas pada pada skripsi ini. Penaksiran parameter-parameter dari distribusi Negative Binomial-Generalized Exponential (NB-GE) menggunakan metode maximum likelihood. Sebagai ilustrasi, digunakan data kecelakaan fatal yang memiliki lebih dari 80% angka nol yang dimodelkan dengan distribusi Negative Binomial-Generalized Exponential (NB-GE).

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Negative Binomial-Generalized Exponential (NB-GE) distribution is a distribution that capable for modeling overdispersion data with extreme excess zeros, which is more than 80% zeros in a data. The distribution is a mixture distribution that obtained by mixing the Negative Binomial (NB) distribution with the Generalized Exponential (GE) distribution. The formation of the Negative Binomial-Generalized Exponential (NB-GE) distribution and the characteristics of the Negative Binomial-Generalized Exponential (NB-GE) distribution such as the probability density function, kth moment, mean, variance, skewness and kurtosis are discussed in this paper. Estimation of the parameters from the Negative Binomial-Generalized Exponential (NB-GE) distribution using the maximum likelihood method. As an illustration, Negative Binomial-Generalized Exponential (NB-GE) distribution used to model the data of fatal crash that has more than 80% zeros."