The Default Risk of Firms Examined with Smooth Support Vector Machines

In the era of Basel II a powerful tool for bankruptcy prognosis is vital for banks. The tool must be precise but also easily adaptable to the bank's objections regarding the relation of false acceptances (Type I error) and false rejections (Type II error). We explore the suitabil- ity of Smooth Support Vector Machines (SSVM), and investigate how important factors such as selection of appropriate accounting ratios (predictors), length of training period and structure of the training sample in°uence the precision of prediction. Furthermore we show that oversampling can be employed to gear the tradeo® between error types. Finally, we illustrate graphically how di®erent variants of SSVM can be used jointly to support the decision task of loan o±cers.Insolvency Prognosis, SVMs, Statistical Learning Theory, Non-parametric Classification models, local time-homogeneity

The Default Risk of Firms Examined with Smooth Support Vector Machines

In the era of Basel II a powerful tool for bankruptcy prognosis is vital for banks. The tool must be precise but also easily adaptable to the bank's objections regarding the relation of false acceptances (Type I error) and false rejections (Type II error). We explore the suitability of Smooth Support Vector Machines (SSVM), and investigate how important factors such as selection of appropriate accounting ratios (predictors), length of training period and structure of the training sample influence the precision of prediction. Furthermore we showthat oversampling can be employed to gear the tradeoff between error types. Finally, we illustrate graphically how different variants of SSVM can be used jointly to support the decision task of loan officers.Insolvency Prognosis, SVMs, Statistical Learning Theory, Non-parametric Classification

A Hierarchical Framework Using Approximated Local Outlier Factor for Efficient Anomaly Detection

AbstractAnomaly detection aims to identify rare events that deviate remarkably from existing data. To satisfy real-world appli- cations, various anomaly detection technologies have been proposed. Due to the resource constraints, such as limited energy, computation ability and memory storage, most of them cannot be directly used in wireless sensor networks (WSNs). In this work, we proposed a hierarchical anomaly detection framework to overcome the challenges of anomaly detection in WSNs. We aim to detect anomalies by the accurate model and the approximated model learned at the re- mote server and sink nodes, respectively. Besides the framework, we also proposed an approximated local outlier factor algorithm, which can be learned at the sink nodes. The proposed algorithm is more efficient in computation and storage by comparing with the standard one. Experimental results verify the feasibility of our proposed method in terms of both accuracy and efficiency

The Default Risk of Firms Examined with Smooth Support Vector Machines

In the era of Basel II a powerful tool for bankruptcy prognosis is vital for banks. The tool must be precise but also easily adaptable to the bank’s objections regarding the relation of false acceptances (Type I error) and false rejections (Type II error). We explore the suitability of Smooth Support Vector Machines (SSVM), and investigate how important factors such as selection of appropriate accounting ratios (predictors), length of training period and structure of the training sample influence the precision of prediction. Furthermore we show that oversampling can be employed to gear the tradeoff between error types. Finally, we illustrate graphically how different variants of SSVM can be used jointly to support the decision task of loan officers

Decision support for the general aviation pilot

©1997 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.Increasing air traffic control (ATC) requirements raises the workload of pilots. Required tasks dictate more “head-in-the-cockpit” computation, which can easily distract a pilot from safe airplane operation. Following eight years of research, we present an on-board PC-based computational system that increases pilot situational awareness, decreases diversion to routine computations, and anticipates upcoming needs. The key to anticipatory flight management is an expert system that uses knowledge of ATC procedures, aircraft operating procedures and limitations, and aircraft performance to infer current flight operating “mode” without direct pilot intervention or input. A flight mode interpreter (FMI) enables automatic display selection, pilot advice, and warning. This paper reports the development of an FMI-based flight management system, called General Aviation Pilot Advisory and Training System (GAPATS), that is being developed jointly by Texas A&M University and Knowledge Based Systems, Inc. Software development is carried out using a fixed-base engineering flight simulator. Pilot participation in all phases of development and evaluation is the norm. Flight tests have begun on an instrumented research light twin owned by the Texas A&M University Flight Mechanics Laboratory

Chalcogenide Glass-on-Graphene Photonics

Two-dimensional (2-D) materials are of tremendous interest to integrated photonics given their singular optical characteristics spanning light emission, modulation, saturable absorption, and nonlinear optics. To harness their optical properties, these atomically thin materials are usually attached onto prefabricated devices via a transfer process. In this paper, we present a new route for 2-D material integration with planar photonics. Central to this approach is the use of chalcogenide glass, a multifunctional material which can be directly deposited and patterned on a wide variety of 2-D materials and can simultaneously function as the light guiding medium, a gate dielectric, and a passivation layer for 2-D materials. Besides claiming improved fabrication yield and throughput compared to the traditional transfer process, our technique also enables unconventional multilayer device geometries optimally designed for enhancing light-matter interactions in the 2-D layers. Capitalizing on this facile integration method, we demonstrate a series of high-performance glass-on-graphene devices including ultra-broadband on-chip polarizers, energy-efficient thermo-optic switches, as well as graphene-based mid-infrared (mid-IR) waveguide-integrated photodetectors and modulators

Detection of Herbicides in Water by SPME

篩測水中殘留農藥，一般常見的萃取方式為液相-液相萃取﹑ 固 相萃取等方式，本研究主要是利用固相微萃取法（solid phase microextraction,SPME），探討在固相微萃取法中，塗覆不同吸附 劑如 poly-acrylate（PA）、poly-dimethylsiloxane（PDMS）等對 吸附之影 響，以氣相層析質譜儀偵測，探討水中2,2-dichloropro- pionic acid （dalapon）﹑2-(4-chloro-2-methylphenoxy)propionic acid（MCPP） ﹑2-methoxy-3,6-dichlorobenzoic acid（dicamba）﹑ 4-chloro-2- methylphenoxyacetic acid（MCPA）﹑2-(2,4-dichloro- phenoxy) propionic acid（2,4-DP）﹑2,4-dichlorophenoxyacetic acid（2,4-D ）﹑2-(2,4,5-trichlorophenoxy)propionic acid （2,4,5-TP） ﹑2,4,5-trichlorophenoxyacetic acid（2,4,5-T）﹑ 2-(sec- butyl)-4,6-dinitrophenol （dinoseb）及4-(2,4-dichloro- phenoxy) butyric acid（2,4-DB）等十種微量除草劑之分析方法。 此類除草 劑分析需經過衍生化之步驟，於實驗中採用重氮甲烷 （diazomethane） 將酸基酯化，將比較衍生化裝置中縱向和橫向方 向直接衍生化 ，結果 以縱向衍生化效果較好。 本實驗探討利用固相微萃取法在萃取水中 微量除草劑時，水中 pH值﹑鹽類的添加﹑環狀糊精的添加﹑腐植酸的濃 度及萃取纖維所 塗覆的吸附劑種類對萃取之影響。實驗結果顯示以塗覆 poly-acrylate 吸附劑的纖維萃取，其效果除了2-(sec-butyl)-4,6- dinitrophenol 外均比塗覆poly-dimethylsiloxane好。萃取效果亦隨水 中鹽類的添 加和酸化而增加，但隨腐植酸濃度增加而降低。環狀糊精的 添加對除 草劑的萃取沒有明顯的助益。以實驗所得萃取條件並以質譜儀 中選擇 離子偵測方式，探討水中十種除草劑偵測極限值，以2- methoxy-3,6- dichlorobenzoic acid﹑2-(2,4,5-trichlorophenoxy) propionic acid 及2,4,5-trichlorophenoxyacetic acid最好，可達 到0.01μg/L。 本實驗亦利用固相微萃取方式，探討除草劑之分解 產物，於加入強 鹼的除草劑靜置四天後，以固相微萃取裝置直接萃取再 以氣相層析質譜 儀分析，可偵測到2,4-二氯酚﹑4-chloro-3- methylphenol﹑2,4,5-三氯 酚﹑四個氯之多氯聯苯及五個氯之多氯聯苯 汁產物。實驗結果可提供水中 微量除草劑分析之參考

Application of Mass Spectrometry in Determination of Trace Analytes in the Complex Matrix

本研究主要利用質譜術於複雜基質中微量成分檢測與定量分析技術之探討。分為三部份，第一部份以多次串聯質譜術鑑定茶葉經高溫處理所產生未知成分，並探討茶中主成分沒食子兒茶素沒食子酸酯(epigallocatechin gallate, EGCG)轉變為沒食子酸(gallic acid, GA)的機制。此方式成功地鑑定三個沒食子兒茶素沒食子酸酯之二聚合相關化合物，並以多次串聯質譜術推論其化學結構以及其他產物；另藉由產物的形成，探討形成沒食子酸之過程與機制。 第二部份以自行改良之吹氣輔助頂空固相微量萃取裝置進行萃取及結合氣相層析質譜儀進行分析。探討水中四種氯酚類化合物之萃取以評估其應用於水中毒性物質分析的可能性。當氮氣流速控制在20 mL/min時透過1公分之中空纖維吹入水中，以85 μm 聚丙烯酸酯（polyacrylate, PA)塗覆材質之纖維於頂空吸附氯酚類化合物30分鐘，並將水溫控制在75℃時可以得到氯酚類化合物之最佳萃取效果。其回收率均大於83%，偵測極限可達sub-fg/mL，偵測極限範圍為0.1 到 0.4 pg/mL之間，相對標準偏差介於4到14%，偵測效果十分理想，並將其應用到垃圾滲出水中微量氯酚類化合物之檢測。以正交表方式評估影響待測物訊號最為顯著之因素，實驗結果顯示萃取溫度的影響最大。 第三部份以雙曲面式四極矩質譜儀應用於尿液中硝甲西泮及其代謝物之檢測。以固相萃取法結合大氣壓化學游離法之高度選擇反應偵測模式進行分析。實驗中將pH 7的尿液樣品一毫升經MP3碟形固相萃取管柱萃取後，再以兩毫升methanol/isopropanol/ NH4OH (78:20:2, v/v/v)混合溶劑沖堤可以得到 最好的萃取效果。其回收率均大於89.6%，最低可確認濃度範圍為0.5 到 1 ng/mL之間，組內(intraday)及組間(interday)精密度以相對標準偏差表示，其範圍分別為3.8%到7.4%以及 10.1%到20.9%之間。為避免偽陽性的分析結果，研究中亦評估將歐盟2002/657/EC對於液相層析串聯質譜術之要求應用於化合物之確認，並將此方法應用於真實尿液樣品之分析。本方法提供一靈敏且可靠的分析方法，可做為尿液中微量違禁藥物分析方法的參考。The main target of this research was to evaluate the reliability of mass spectrometryin trace analysis of analytes in the complex matrices. The first investigation evalutes the releasing of gallic acid (GA) from (-)-epigallocatechin gallate (EGCG) in old oolong tea by data-dependent multiple-stage mass spectrometry. The possibility of releasing GA from EGCG in old tea preparations was supported by an in vitro observation of GA degraded from EGCG under heating conditions mimicking the drying process. Negative electrospray ionization with the data-dependent mode of MSn was used to study the formation pathway of GA in old oolong tea. The MSn data show the possibility of GA released from the dimer of EGCG. The second investigation evaluates a simple, sensitive and effective method for simultaneous determination of chlorophenols in aqueous samples using purge-assisted headspace solid-phase microextraction (PA/HS-SPME) coupled to gas chromatography-mass spectrometry (GC/MS). In the new method, purging the sample enhances the removal of the trace chlorophenols without derivatization from the matrices to the headspace. Extraction parameters including extraction temperature, purge gas flow rate and extraction time were systematically investigated. Under optimal conditions, the relative standard deviations (RSDs) were 4-11% at 50 pg/mL and 5-14% at 5 pg/mL, respectively. The recoveries were above 83%. Detection limits were determined at the fg level, ranged from 0.1-0.4 pg/mL. The proposed method was successfully applied to the analysis of chlorophenols in landfill leachate. The most affected factor among four main factors was also evaluated via L9(3)4 orthogonal array. In the third evaluation, a rapid method was developed for screening nimetazepam and its metabolites, 7-aminonimetazepam (7-AN) and nitrazepam, in urine using solid-phase extraction combined with liquid chromatography-atmospheric pressure chemical ionization/tandem mass spectrometry (SPE-LC-APCI/MS/MS) with enhanced mass resolution. High extraction efficiency was obtained using a MP3 disk cartridge to extract analytes in a pH 7 urine sample, which was then eluted with a solvent mixture, methanol/isopropanol/NH4OH (78/20/2,v/v/v). Two mass transitions of every drug were utilized for qualitative and quantitative purposes. The limits of confirmation (LOC) were 0.5-1.0 ng/ mL. The linear concentration range was 0.5-50 ng/mL, with coefficients of determination over 0.995. Intraday and interday precision represented in RSD% were in the range of 3.8-7.4% and 10.1-20.9%, respectively. The proposed method offers a rapid and low ng/mL sensitive method for analyzing nimetazepam and its metabolites in urine. The feasibility of applying the proposed method to identify nimetazepam and its metabolites in real samples was examined by analyzing urine samples from patients.謝誌 I 中文摘要 III 英文摘要 V 目錄 VI 表目錄 XI 圖目錄 XII 第一章、緒論 1 1.1、質譜術原理 1 1.2、質譜儀的基本構造 3 1.3、層析質譜術 9 1.3.1、氣相層析質譜術 9 1.3.2、液相層析串聯質譜術 11 1.3.2.1、電灑游離法 16 1.3.2.1、大氣壓化學游離法 18 1.3.3、質量分析器 21 1.3.3.1、四極矩質量分析器 21 1.3.3.2、離子阱質量分析器 23 1.4、樣品前處理 25 1.4.1、固相萃取法 27 1.4.2、固相微量萃取法 30 1.5、研究目標 34 1.6、參考資料 36 第二章、多次串聯質譜術探討烏龍茶中表沒食子兒茶素沒食子酸酯釋出沒食子酸的機制 39 2.1、前言 39 2.2、實驗部份 43 2.2.1、藥品及溶劑 43 2.2.2、實驗器材與儀器設備 44 2.3、結果與討論 45 2.3.1、表沒食子兒茶素沒食子酸酯之負離子電灑法質譜圖 45 2.3.2、資料依存多次串聯質譜術 48 2.3.3、未知物之結構鑑定 48 2.3.4、沒食子酸的生成路徑 53 2.4、結論 63 2.5、參考資料 63 第三章、吹氣輔助頂空固相微量萃取法於水中氯酚類化合物之檢測 66 3.1、前言 66 3.2、實驗部份 69 3.2.1、藥品、溶劑及試藥 69 3.2.2、實驗器材與儀器設備 70 3.3、結果與討論 71 3.3.1、吹氣輔助的影響 73 3.3.2、氮氣流速的影響 73 3.3.3、多孔中空纖維薄膜的長度 76 3.3.4、萃取溫度及時間 76 3.3.5、方法確效 76 3.3.6、真實水樣分析 80 3.3.7、因素重要性之評估 80 3.4、結論 84 3.5、參考資料 85 第四章、液相層析串聯質譜術於尿液中硝甲西泮及其代謝物之篩檢 87 4.1、前言 87 4.2、實驗部份 91 4.2.1、藥品 91 4.2.2、試劑 91 4.2.3、藥品配置 92 4.2.4、真實尿液樣品 92 4.2.5、實驗器材與儀器設備 92 4.3、結果與討論 94 4.3.1、液相層析串離質譜分析條件 94 4.3.2、四極矩之半高寬設定 98 4.3.3、固相萃取法之最佳化條件 104 4.3.4、方法確效 104 4.3.5、真實尿液樣品分析 106 4.4、結論 111 4.5、參考資料 111 第五章、總結 11