Fission life-time calculation using a complex absorbing potential

A comparison between the semi-classical approximation and the full quantum calculation with a complex absorbing potential is made with a model of the fission of 258Fm. The potential barrier is obtained with the constrained Skyrme HF+BCS theory. The life-time obtained by the two calculations agree with each other the difference being only by 25%.Comment: 5 pages, 2 figures, Conference proceedings of CNR*15 workshop, Tokyo, October 2015 to be published in EPJ Web of Conference

コーポレート・ガバナンス評価指標を用いた企業不祥事の分析 ~企業不祥事を予測できるのか~

早稲田大学Master of Business Administrationmaster thesi

The Study on the Partial Least Squares Regression, Principal Component Regression, and Neural Networks to Analyze the Near-Infrared Spectrum Data

近年來利用近紅外光分析儀等光譜儀器，來測定物質中的各種化學成分含量，有越來越普遍的趨勢，而分析這類型的資料，需要藉助多變數檢量的技巧，本研究利用主成分迴歸（principal component regression, PCR）、淨最小平方迴歸（partial least squares regression, PLSR）以及類神經網路（neural networks , NN）三種檢量模式，以及利用Gnanadesikan（1997）的概念，改良主成分分析及淨最小平方法，並藉著所得到的加性主成分（additive principal components），以及線性二次淨最小平方法（linear-quadratic partial least squares），針對糙米粒粗蛋白質含量的近紅外線光譜資料，進行檢量模式的建立，結果Gnanadesikan（1997）的擴充 矩陣概念，確實能改善PCR模式，不過當與PLSR相比時，PLSR模式的表現仍較佳。 除此之外，本文也探討眾多非線性檢量技巧之一的類神經網路，並分別以原始351個波段、主成分分析之成分以及淨最小平方法的得點 ，當作輸入的變數，但是得到的NN模式表現並不理想，因此再利用PCR、PLSR模式的迴歸係數，取代NN輸入層與隱藏層之間初始的加權值，來改良NN模式，最後得到的結果，以PLSR模式中的迴歸係數為輸入層與隱藏層之間初始的加權值之NN表現最好，並且也能加快網路的學習速度，因此以迴歸係數來改良NN，的確能夠改善強NN建立模式的能力。Near-infrared reflectance spectroscopy (NIRS) is widespreadly used for quantitative applications of chemometrics in recent years. In this work, calibration methods including principal component regression (PCR), partial least squares regression (PLSR), additive principal component regression (APCR), linear-quadratic partial least squares regression (LQ-PLSR) and neural networks (NN) were used in conjunction with the near-infrared reflectance spectroscopy technique to determine the protein content of brown rice. Some calibration methods are insensitive to the effects of non-linearity. Such is the case with the model developed by Gnanadesikan (1977), which expands the X matrix with the squares of the variables. The projection of the additive principal component analysis (APCs) and linear-quadratic partial least squares (LQ-PLS) components on a surface in the expanded space corresponds to that of the original X matrices in a quadratic space. The LQ-PLS regression preserves a linear internal relationship between the scores of the X and Y matrices. Based on the results, the additive principal component regression performed better than principal component regression and the performance of partial least squares regression was the best. In addition, the result of applying the scores of the principal component analysis and partial least squares to the neural networks was compared. It was found that the neural networks approach was not effective. Hence the regression coefficient of PCR and PLSR were used as the initial weights between the input and hidden layer in neural networks model. It was shown that the neural networks model based on the regression coefficient of PLSR performed most effective, so it was the best choice of calibration modeling.中文摘要………………………………………………… I 英文摘要………………………………………………… II 第壹章 前言………………………………………………1 第貳章 前人研究…………………………………………4 一、線性檢量方法……………………………………… 4 （一）主成分迴歸……………………………………… 4 （二）淨最小平方法…………………………………… 6 二、非線性檢量方法…………………………………… 12 （一）類神經網路……………………………………… 13 1.類神經網路的原理…………………………………… 13 2.倒傳遞網路…………………………………………… 16 (1).倒傳遞網路演算法………………………………… 19 (2).倒傳遞網路的參數設定…………………………… 23 三、預測模式的驗證…………………………………… 25 （一）內部驗證法……………………………………… 27 （二）外部驗證法……………………………………… 28 第參章 實例研究..………………………………………29 一、試驗資料分析……………………………………… 29 二、實例研究一………………………………………… 32 （一）目的與方法……………………………………… 32 （二）結果與…………………………………………… 33 三、實例研究二………………………………………… 44 （一）目的與方法……………………………………… 44 （二）結果與討論……………………………………… 45 四、實例研究三………………………………………… 57 （一）目的與方法……………………………………… 57 （二）結果與討論……………………………………… 58 第肆章 綜合討論…………………………………………78 參考文獻………………………………………………… 81 附錄 SAS/IML POGRAM……………………………………84 A.1 PCR……………………………………………………84 A.2 APCR………………………………………………… 85 A.3 PLSR………………………………………………… 86 A.4 LQ-PLSR………………………………………………87 A.5 NN-1………………………………………………… 88 A.6 NN-2………………………………………………… 92 A.7 NN-3………………………………………………… 96 A.8 PC-NN……………………………………………… 100 A.9 PLS-NN………………………………………………10

Maize Pollen Dispersal Model: Using Nonlinear, Quasi-mechanistic Models and Neural Networks to Evaluate the Recommended Isolation Distance for Coexistence between GM and Non-GM Crops in Taiwan

在過去的二十年，許多基因改造 (genetically modified, GM) 作物已被成功開發上市，並進入商業化的田間生產。種植在開放田間的基因改造作物，可能與野生近緣種或傳統非基改品種的作物發生雜交，導致基因流和基因污染。由於花粉傳播是影響玉米基因流的主要因素，瞭解在田野中發生異交 (cross-pollination,CP) 的程度，可以幫助建立基因改造與非基因改造作物間，共存的隔離距離和田區種植的配置。本研究於 2009 至 2011 年間，在台灣本土進行實際玉米田間試驗，考慮七種不同的場景佈局，調查田間種植時玉米花粉流的程度。試驗田區設置位於台中霧豐的農業試驗所 (Taiwan Agricultural Research Institute, TARI) (24°1'N,120°41'E)，及位於嘉義朴子的台南區農業改良場 (Tainan District Agricultural Research and Extension Station , TNDAIS) 朴子分場 (23°47'N, 120°26'E)。利用此本土田間試驗的異交率資料，以及數種非線性和擬機械模式，建立花粉接受親與花粉貢獻親間之距離與異交程度的關係。此外，在模式建立同時，也採用重複抽樣的技術，進行模式的評估及驗證。在第三章中，NIG (normal inverse Gaussian)模式在所使用的二維與一維模式中，擁有最好的建模表現。根據保守的結果，田區模擬的異交汙染率在 95%信心水準，小於 0.9%的情況下 (kernel-number-based量測法)，其所需的距離分別為 NIG 模式的 25 公尺。此外，在 95%信心水準小於 3%與 5%的情況下 (kernel-number-based 量測方法)，其所需的距離為 NIG 模式的 15 與 10 公尺。在第四章中，我們使用三種具有不同的輸入的類神經網絡 (artificial neural network, ANN）模型建立花粉飄散模式：原始的輸入神經網絡 (neural network,NN)、主成分神經網絡 (principal components neural network, PCNN)、淨最小平方 神經網絡 （partial least squared neural network, PLSNN）模型。根據 ANN 模型田區 模 擬 結 果 ， 異 交 汙 染 率 在 95% 信 心 水 準 小 於 0.9% 的 情 況 下(kernel-number-based 量測法)，其所需的距離分別為 NN 模型的 40 公尺、PCNN模型與 PLSNN 模型的 35 公尺。在小於 3%的情況下 (kernel-number-based 量測 法)，其所需的距離分別為在 NN 模式與 PCNN 模式的 30 公尺、以及 PLSNN 模式的 35 公尺。在 95%信心水準小於 5%的情況下 (kernel-number-based 量測法)，其所需的距離分別為在 NN 模式與 PLSNN 模式的 30 公尺、以及 PCNN 模式的25 公尺。此外，我們亦發展了一套方法，可應用於解釋 ANN 模型的黑盒子，根據本研究的所建立的 3 種 ANN 模型，其模型訓練後的記憶特徵可藉由因素分析(factor analysis) 的結果進行說明與解釋。綜合各項結果，同時考慮模型效率和保守的預測隔離距離，本研究建議在大多數的情況下，在 95%信心水準下，30 公尺、35 公尺、40 公尺的隔離距離即分別足以維持非基因改造玉米田區收穫物，基改基因污染率小於 5%、3%與 0.9%的門檻。In the past two decades, numerous genetically modified (GM) crops have been successfully developed and released into commercial production. GM crops planted in an open field can outcross with feral or conventional crops, leading to gene flow and gene contamination. Because pollen dispersal is the major factor that affects gene flow in maize, the exploration of cross-pollination (CP) in the field may facilitate the establishment of the isolation distance and field arrangements to allow the coexistence of GM and non-GM crops. In this study, we conducted local field experiments to measure pollen flow in maize by using seven scenarios of diverse field-layout patterns during 2009 and 2011. The experimental fields were located at Wufeng (24°1'N, 120°41'E) managed by the Taiwan Agricultural Research Institute (TARI) and at Puzih (23°47'N, 120°26'E) belonging to the Potzu Branch Station of the Tainan District Agricultural Research and Extension Station (TNDAIS). The CP rates collected from local maize field trials were used to establish the relationship between the extent of CP in recipient and distances from the pollen source by using several nonlinear and quasi-mechanistic models. In addition, the resampling method was used for both evaluating and verifying the models. The normal inverse Gaussian (NIG) model had a better performance among the 2D and 1D model. According to the conservative result, the 95% upper confidence limits of simulated CP rates were below 0.9% (kernel-number-based approach) at the isolation distance of 25 m on the basis of the NIG model. Additionally, the simulated CP rates were <3% and <5% beyond 15 m and 10 m for NIG model at a 95% confidence level, respectively. In chapter 4, we used three artificial neural network (ANN) models featuring distinct inputs to establish the pollen dispersal models: the neural network (NN), principal components neural network (PCNN), and partial least squared neural network (PLSNN) models. Our analyses revealed that the 95% upper confidence limits of the CP rate (as per the kernel-number-based approach) in field simulations were <0.9% at the isolation distances of 40 m, 35 m, and 35 m predicted using the NN, PCNN, and PLSNN models, respectively. The simulated CP rates were <3% at the isolation distances of 30 m, 30 m, and 35 m predicted by NN, PCNN, and PLSNN, respectively. The simulated CP rates predicted using the NN, PCNN, and PLSNN models were <5% at a 95% confidence level at distances greater than 30 m, 25 m, and 30 m, respectively. Furthermore, we developed a technique to interpret the black box of ANN model, and the result showed that various characteristics of the data can be explained by using factor analysis in 3 ANN models. In conclusion, to consider both model performance and conservative isolation distance in most cases, a recommended isolation distances of 30 m, 35 m, and 40 m are adequate for maintaining the GM contamination rate of the total harvest of a field at <5%, <3%, and <0.9% at a 95% confidence level, respectively.CHAPTER 1 COEXISTENCE BETWEEN GM AND NON-GM MAIZE ......1 1.1 INTRODUCTION........................1 1.2 FIELD EXPERIMENTAL STUDIES .....................2 1.3 MODELING APPROACH ..............................4 1.4 MEASUREMENT UNITS FOR GENETICALLY MODIFIED ORGANISMS.............................................5 1.5 COEXISTENCE MEASURES AND MANAGEMENT...................5 1.6 MAIZE IN TAIWAN .........................8 CHAPTER 2 FIELD EXPERIMENT ON CROSS-POLLINATION OF MAIZE IN TAIWAN ......................... 17 2.1 INTRODUCTION..................17 2.2 MATERIALS AND METHODS ...........................17 2.2.1 Experimental fields .............................17 2.2.2 Meteorological data.............................18 2.2.3 Sampling method........................... 19 2.2.3.1 Scenario W1................................19 2.2.3.2 Scenario W2.................................19 2.2.3.3 Scenarios W3 and W4 .............................20 2.2.3.4 Scenarios P1, P2, and P3.........................20 2.2.4 CP rate and distance definition..............20 2.3 RESULTS AND DISCUSSION ..............................21 2.3.1 Experimental scenarios.....................21 2.3.2 Extent of observed CP rate.........................26 2.4 CONCLUSION ...............................28 CHAPTER 3 USING EMPIRICAL AND QUASI-MECHANISTIC MODELS TO ASSESS THE CROSS-POLLINATION OF MAIZE BY POLLEN-MEDIATED GENE FLOW AT A FIELD SCALE...................................60 3.1 INTRODUCTION.........................60 3.2 MATERIALS AND METHODS ...........................60 3.2.1 Fit models ..................................60 3.2.2 Model evaluation..................................65 3.2.3 Evaluation criteria .........................66 3.3 RESULTS AND DISCUSSION .............................68 3.3.1 Meteorological data and experimental sites .....................................68 3.3.2 Parameter estimation ...........................69 3.3.3 Model selection...............................72 3.3.4 Field simulation of the NIG models ................74 3.4 CONCLUSION ....................................76 CHAPTER 4 DETERMINING AND INTERPRETING THE EXTENT OF MAIZE CROSS-POLLINATION AT A FIELD SCALE BY USING ARTIFICIAL NEURAL NETWORKS ................................... 97 4.1 INTRODUCTION.....................97 4.2 MATERIALS AND METHODS ...........................98 4.2.1 Artificial neural network models and input variables.........................................98 4.2.2 Resampling simulation..........................101 4.2.2.1 Model evaluation ........................... 101 4.2.2.2 Model interpretation ...........................101 4.2.3 Interpretation of input variables in artificial neural network models ............................ 101 4.2.3.1 Sensitivity analysis of input variables..........102 4.2.3.2 The black box of weight and bias ...............102 4.2.4 Simulation analysis ........................... 104 4.2.4.1 Single recipient point ..........................104 4.2.4.2 Total harvest of recipient field ...............105 4.3 RESULTS AND DISCUSSION ..............................105 4.3.1 Model selection....................106 4.3.2 Factor analysis and interpretation of artificial neural network models......................... 108 4.3.2.1 Neural network model ...........................110 4.3.2.2 Principal components neural network model .......112 4.3.2.3 Partial least squares neural network model ......113 4.3.3 Field simulation ..............................116 4.3.3.1 Single recipient point.................... 116 4.3.3.2 Whole recipient field ...........................119 4.4 CONCLUSION .......................................122 CHAPTER 5 CONCLUDING REMARKS AND FUTURE WORKS ....................161 5.1 CONCLUDING REMARKS.......................... 161 5.1.1 Filed experiment .................................161 5.1.2 Nonlinear and quasi-mechanistic models ............162 5.1.3 Artificial neural network models ..............162 5.2 COEXISTENCE BETWEEN GM AND NON-GM MAIZE IN TAIWAN............................................. 164 5.3 FUTURE WORKS.....................166 REFERENCES ................................168 APPENDIX A.............................180 APPENDIX B........................... 189 APPENDIX C .............................21

Business Model of Online Third Party Payment in Taiwan

2012年12月，台灣第三方支付服務業正式被經濟部所明定，然而台灣線上第三方支付服務因為電子商務經營環境之特殊性，並無法完全移植國外PayPal與AliPay之經驗。本研究透過這兩年來廣泛用於討論新創事業的Business Canvas來分析台灣三家第三方支付服務公司之商業模式，搭配經營環境之現況，提出未來可行之行動方向。 本研究認為，台灣第三方支付服務業之「安全」與「便利」已經成為基本的價值主張，業者須提供更加多元與專屬性的服務，以符合特定目標市場之需求，並產生交易手續費之外的收益流以維持企業之營運，就政府立場而言，應適時地放寬法規之限制，並透過資策會等輔導單位協助台灣第三方支付服務業與中國等海外市場銜接，擴大業務規模。本研究屬於初探性質，可作為未來台灣第三方支付服務相關研究之基礎。Until Dec. 2012, Ministry of Economic Affairs, R.O.C. made an announcement about the definition of third party payment services in Taiwan. Due to Taiwan’s specific e-commerce business environment, it’s hard to directly duplicate those famous third part payment services like PayPal and AliPay into Taiwan market. This research used the well-known business model analysis tool: Business Canvas to analyze three Taiwan’s third party payment cases. The purpose of this research would like to give some suggestions for Taiwan’s third party payment providers and related authorities. In the past, “Safety” and “Convenience” are the value third payment providers emphasized most. However they all turn out to be the fundamental value in Taiwan’s business environment so that Taiwan third party payment providers must seek to other value propositions like “diverse services” and generate other revenues rather than credit card transaction fee. For related authorities, it’s important to loosen the limit of laws and regulations meetly and guide and support Taiwan third party payment providers for entering overseas market like China. This research plays a role of an advanced research in Taiwan third party payment business model

The eFFects of inFrasound and magnetic Field upon Aspergillus Niger,Enzymes and distilled water

用适当的次声频率和声压来处理黑曲霉孢子（微生物），能使其柠檬酸率增加7%左右，同时用磁场和低频磁场作对比处理，其结果亦同。此外，用此类方法处理五种生物酶和蒸馏水时，其紫外光谱的特征蜂的吸光值均有所增加，与增加酶在缓冲剂中的浓度或水的温度作用相同。而在用此二法处理黑曲菌孢子的发酵液时，发现其主酶（α—淀粉酶）的活力及菌丝体重量也对应地增加，和用同法处理此类纯酶时其活力不能增加的一些报道加以对比。InFrasound and magnetic Field are used to treat Aspergillus Niger.And we Find that the production rate of critic acid, the activity of enzyme and the weight of hypha are all increased at the same rate.Also,these two methods are used to treat some enzymes and distilled water and we get an increase in absorbands of UV spectra of them, similar to the eFFects obtained, by increasing the concentration of onzymes or by increasing the temperature of water