Theoretical Studies of the Dependence of Chemical Reaction on Tautomeric Form of His64 in the Active Site of Human Carbonic Anhydrase Ⅱ

13301甲第4143号博士（理学）金沢大学博士論文要旨Abstract

Quantum control of high harmonic generation in anharmonic potential

In this work, we first aim to realize the high harmonic generation(HHG) spectrum in a classical and quantum treatment, and then to achieve the quantum control of the HHG spectrum. Here, we consider the HHG from the Duffing oscillator well-known as a typical anharmonic system. The HHG induced by the intense laser is a nonlinear optical process, where a molecular system absorbs n photons with energy ω and emits one high energy photon with energy nω. The light generated by HHG is a promising light source with an attosecond ultrashort pulse, which could be used to analyze the electronic structure and motion in molecule. First, we perform a classical simulation to explain the structure of the HHG spectrum. Then, we investigate quantum-mechanically the mechanism of HHG, and compare the classical and quantum result. In quantum treatment, we apply the Fourier Grid Method (FGM) to obtain the eigenvalues and wavefuntion of the Duffing oscillator and use the Split Operator Method (SOM) to solve the time-dependent Schr¨odinger equation. Here we study the laser intensity dependence of the HHG with a single laser pulse, and then from the interference point of view, we focus on the role of the relative phase of two laser pulses and simulate to find out the phase dependence of the HHG. Finally we investigate the laser control of the HHG by changing the initial quantum state, which is a kind of a wave packet due to the superimposition of few eigenstates

Theoretical Studies of the Dependence of Chemical Reaction on Tautomeric Form of His64 in the Active Site of Human Carbonic Anhydrase Ⅱ

13301甲第4143号博士（理学）金沢大学博士論文本文Ful

Fully Convolutional Variational Autoencoder For Feature Extraction Of Fire Detection System

This paper proposes a fully convolutional variational autoencoder (VAE) for features extraction from a large-scale dataset of fire images. The dataset will be used to train the deep learning algorithm to detect fire and smoke. The features extraction is used to tackle the curse of dimensionality, which is the common issue in training deep learning with huge datasets. Features extraction aims to reduce the dimension of the dataset significantly without losing too much essential information. Variational autoencoders (VAEs) are powerfull generative model, which can be used for dimension reduction. VAEs work better than any other methods available for this purpose because they can explore variations on the data in a specific direction

Theoretical study of a π-stacking interaction in carbonic anhydrase

Human carbonic anhydrase II (HCA II) catalyses the reversible hydration of CO2. In this enzyme, the imidazole ring of histidine at position 64 (His64) functions to transfer the productive proton from the zinc-bound water to the buffer molecule in bulk-water. X-ray data of HCA II show that His64 has two types of side chain orientations, ”in” and ”out”, representing the direction of the imidazole ring toward and away from the active site, respectively. Maupin et al. reported that the imidazole of His64 can be rotated in a model system of the active site to clarify the proton transfer of catalytic mechanism. However, the indole ring of tryptophan at position 5 (Trp5) that is located near the ”out” of the imidazole ring of His64 was not considered in the model system. In this study, in order to estimate detailed rotational properties of His64, we constructed two His64-containing models with and without Trp5, and then simulate the constructed structures by using MP2 method and 6-311++G(d,p) basis sets. This allows us to tentatively determine the potential energies of the π-stacking interaction of the imidazole with the indole in relation to the side chain rotation of His64. The result indicates that the π-stacking interaction causes an increase of the energy barrier between ”in” and ”out” conformations, implying that the rotational motion of His64 is not relevant to explain the proton transfer during catalysis. Alternatively, a steady position of His64 would be needed in the proton transfer in catalytic mechanism of HCA II

Peningkatan Literasi Komputer Melalui Pelatihan Micosoft Excel Advanced Untuk Efisiensi Pekerjaan di Instansi Pemerintahan

Era digital melahirkan berbagai potensi dan tantangan yang memasuki berbagai bidang seperti politik, ekonomi, sosial budaya, pertahanan dan keamanan serta teknologi informasi. Pemerintahan atau sistem birokrasi di Indonesia juga  tidak luput dari  potensi dan tantangan perkembangan era digital ini. Salah satu tantangan besar yang harus dihadapi oleh sistem birokrasi Indonesia adalah tuntutan lahirnya inovasi yang berorientasi pada teknologi digital, sehingga inovasi ini diharapkan dapat memudahkan Aparatur Sipil Negara  (ASN)  dalam melaksanakan tugas dan fungsinya. Kemudahan segala pekerjaan dengan berbasis aplikasi dan teknologi ini selanjutnya diharapkan mampu memberikan pelayanan yang lebih optimal kepada masyarakat. Menanggapi tantangan ini, civitas Universitas Pertamina melalui program Pengabdian Kepada Masyarakat (PKM) berbagi pengetahuan, ilmu dan keahlian dalam penggunakan Microsoft Excel untuk mendukung efektivitas pengerjaan pekerjaan harian pada ASN Kantor Pelayanan Kekayaan Negara dan Lelang (KPKNL) Bekasi. Kegiatan ini diharapkan meningkatkan  kemampuan Sumber Daya Manusia di kalangan KPKNL Bekasi, meningkatkan efektifitas dan efisiensi pelaksanaan tugas dan fungsi sehari-hari, serta menjadi katalis dalam munculnya inovasi yang berorientasi pada  teknologi digital

Robust Principal Component Analysis for Feature Extraction of Fire Detection System

Fire detection system with deep learning-based computer vision (DLCV *) algorithm is proposed in this paper. It uses visible light sensor charged-coupled device (CCD) which can be usually found in closed circuit television camera (CCTV). The performance of this DLCV fire detection depends on how many fire image datasets are trained that might lead to the curse of dimensionality. To tackle the curse of dimensionality, Principal Component Analysis (PCA) will be used. PCA is a technique for feature extraction in which the dimensionality of such datasets is reduced significantly. This will results in increasing interpretability but at the same time minimizing information loss