No description available.

Student selection test in Turkey compose of two parts. The purpose of the first part is to assess students&rsquo / higher order thinking skills like analytical thinking, interpretation and reasoning about elementary school curriculum and 9th grade curricula objectives. On the other hand, second part of the test aims to assess students&rsquo / higher order thinking skills given in the high school curriculum. The main aim of this thesis is to analyze to what extend 2006, 2007 and 2008 student selection tests biology items assess higher order cognitive skills. In accordance with this purpose, elementary and high school curriculum and the appropriateness of the questions in the student selection test with the educational objectives of the curriculum are examined. In addition, dimensions of 2006, 2007, and 2008 SST biology items are examined by Exploratory Component Analysis and Confirmatory Component Analysis techniques. The result of those analysis revealed that SST biology items mostly focus on remembering skill and fail to assess higher order thinking skills. Additionally, there is not any consistency among 2006, 2007, and 2008 SSTs biology items in terms of dimensions which means there is not any construct in biology subtests of SSTs. The other aim of the present study is to identify how much academic and non-academic factors explain the biology achievement. While for academic factors reading comprehension, mathematics, physics, and chemistry achievements of students are used, age, gender, and school type are used for non-academic factors. Findings of the research revealed that academic factors, especially chemistry achievement, have significant affect on biology achievement. In terms of non-academic factors, graduating from selecting high school has important role for biology achievement. Additionally, older students and girls tend to have higher grades in biology.

解約風險為壽險公司承保風險中最重要之風險，文獻指出若於保單定價時忽略解約率可能為動態，將影響壽險公司損益、資產配置、資金流動性及風險管理計畫。本研究將以保費及準備金試算進行實證研究，觀察以傳統精算方式定價(忽略解約率為動態)將對保費及準備金之計算造成多少誤差。 本研究首先使用台灣壽險業1987年至2011年之生死合險、終身壽險解約率資料，並透過主成分分析、模擬主成分分數並將其轉回各保單年度解約率，以完成動態解約率之模擬。接著以30歲男性為對象，計算不同情境下之保費及準備金。最後比較不同情境下之保費及準備金差異以了解忽略隨機解約率對保單定價之影響程度。 實證結果顯示，考量隨機解約率與否對生死合險保費計算稍有影響但不明顯，但若長期累積觀察，是否考量隨機解約率對生死合險準備金有顯著影響。本研究使用之終身壽險解約率模型與利率無關、僅受其自身隨機效果影響，故是否考量隨機解約率對終身壽險保費及準備金之影響程度皆不大。

過去有相當多的文獻針對解約率建立模型，但由於資料取得之困難，鮮少文獻針對不同保單年度之解約率進行分析，本研究將以台灣壽險業資料分析不同保單年度之解約率行為，期望能找出解約率之期限結構，提供壽險業者訂價或風險管理之參考依據。 　　本研究使用台灣壽險業1987年至2011年間之生死合險及終身壽險資料，透過資料分析顯示兩險種之解約率關聯性不大，且應將繳別分為三類進行分析，分別為不分繳別、月繳及年繳和半年繳及季繳三類，針對各保單年度進行主成分分析，結果顯示皆需6至8個主成分方可達到90%之解釋力，並透過ARMA模型檢驗選定之主成分與總體經濟變數間之關聯性，進而觀察是否符合利率假說及緊急資金假說，最後透過VAR模型或ARMA模型模擬總體經濟變數和各主成分之分數，並利用主成分分析之結果將主成分分數轉換回保單年度變數，完成各保單年度解約率之模擬，建構出台灣壽險業解約率之期限結構。

This cross-sectional study aimed to generate an exploratory functional model of deliberate self-harm (DSH) occurring among a South African student population (n = 603) using Principle Component Analysis (PCA). A battery of instruments, including the Deliberate Self-Harm Inventory (DSHI) and the Functional Assessment of Self-Mutilation (FASM) were administered to students. Following Nock and Prinstein’s (2004) four-factor model on the functions of DSH, self-reported reasons for DSH on the FASM were conceptualised in light of the reinforcement mechanisms of social learning theory. PCA revealed a four-factor model for the reasons students engaged in DSH, constituent of the following components: social positive reinforcement, social negative reinforcement, automatic reinforcement, and reinforcement regarding self-image. A unique factor manifests in the self-image reinforcement component, suggesting divergence in the functions of DSH between student and adolescent populations. / Dissertation (MA)--University of Pretoria, 2013. / gm2014 / Psychology / unrestricted

Indiana University-Purdue University Indianapolis (IUPUI) / The link between volatile organic compounds (VOCs) from breath and various diseases and specific conditions has been identified since long by the researchers. Canine studies and breath sample analysis on Gas chromatography/ Mass Spectroscopy has proven that there are VOCs in the breath that can detect and potentially predict hypoglycemia. This project aims at developing a smart nanosensor system to detect hypoglycemia from human breath. The sensor system comprises of 1-Mercapto-(triethylene glycol) methyl ether functionalized goldnanoparticle (EGNPs) sensors coated with polyetherimide (PEI) and poly(vinylidene fluoride -hexafluoropropylene) (PVDF-HFP) and polymer composite sensor made from PVDF-HFP-Carbon Black (PVDF-HFP/CB), an interface circuit that performs signal conditioning and amplification, and a microcontroller with Bluetooth Low Energy (BLE) to control the interface circuit and communicate with an external personal digital assistant. The sensors were fabricated and tested with 5 VOCs in dry air and simulated breath (a mixture of air, small portion of acetone, ethanol at high humidity) to investigate sensitivity and selectivity. The name of the VOCs is not disclosed herein but these VOCs have been identified in-breath and are identified as potential biomarkers for other diseases as well. The sensor hydrophobicity has been studied using contact angle measurement. The GNPs size was verified using Ultra-Violent-Visible (UV-VIS) Spectroscopy. Field Emission Scanning Electron Microscope (FESEM) image is used to show GNPs embedded in the polymer film. The sensors sensitivity increases by more than 400\% in an environment with relative humidity (RH) of 93\% and the sensors show selectivity towards VOCs of interest. The interface circuit was designed on Eagle PCB and was fabricated using a two-layer PCB. The fabricated interface circuit was simulated with variable resistance and was verified with experiments. The system is also tested at different power source voltages and it was found that the system performance is optimum at more than 5 volts. The sensor fabrication, testing methods, and results are presented and discussed along with interface circuit design, fabrication, and characterization. / 2022-05-8

No description available.

Les techniques d'analyse du visage nécessitent généralement une représentation pertinente des images, notamment en passant par des techniques de réduction de la dimension, intégrées dans des schémas plus globaux, et qui visent à capturer les caractéristiques discriminantes des signaux. Dans cette thèse, nous fournissons d'abord une vue générale sur l'état de l'art de ces modèles, puis nous appliquons une nouvelle méthode intégrant une approche non-linéaire, Kernel Similarity Principle Component Analysis (KS-PCA), aux Modèles Actifs d'Apparence (AAMs), pour modéliser l'apparence d'un visage dans des conditions d'illumination variables. L'algorithme proposé améliore notablement les résultats obtenus par l'utilisation d'une transformation PCA linéaire traditionnelle, que ce soit pour la capture des caractéristiques saillantes, produites par les variations d'illumination, ou pour la reconstruction des visages. Nous considérons aussi le problème de la classification automatiquement des poses des visages pour différentes vues et différentes illumination, avec occlusion et bruit. Basé sur les méthodes des représentations parcimonieuses, nous proposons deux cadres d'apprentissage de dictionnaire pour ce problème. Une première méthode vise la classification de poses à l'aide d'une représentation parcimonieuse active (Active Sparse Representation ASRC). En fait, un dictionnaire est construit grâce à un modèle linéaire, l'Incremental Principle Component Analysis (Incremental PCA), qui a tendance à diminuer la redondance intra-classe qui peut affecter la performance de la classification, tout en gardant la redondance inter-classes, qui elle, est critique pour les représentations parcimonieuses. La seconde approche proposée est un modèle des représentations parcimonieuses basé sur le Dictionary-Learning Sparse Representation (DLSR), qui cherche à intégrer la prise en compte du critère de la classification dans le processus d'apprentissage du dictionnaire. Nous faisons appel dans cette partie à l'algorithme K-SVD. Nos résultats expérimentaux montrent la performance de ces deux méthodes d'apprentissage de dictionnaire. Enfin, nous proposons un nouveau schéma pour l'apprentissage de dictionnaire adapté à la normalisation de l'illumination (Dictionary Learning for Illumination Normalization: DLIN). L'approche ici consiste à construire une paire de dictionnaires avec une représentation parcimonieuse. Ces dictionnaires sont construits respectivement à partir de visages illuminées normalement et irrégulièrement, puis optimisés de manière conjointe. Nous utilisons un modèle de mixture de Gaussiennes (GMM) pour augmenter la capacité à modéliser des données avec des distributions plus complexes. Les résultats expérimentaux démontrent l'efficacité de notre approche pour la normalisation d'illumination. / Face analysis techniques commonly require a proper representation of images by means of dimensionality reduction leading to embedded manifolds, which aims at capturing relevant characteristics of the signals. In this thesis, we first provide a comprehensive survey on the state of the art of embedded manifold models. Then, we introduce a novel non-linear embedding method, the Kernel Similarity Principal Component Analysis (KS-PCA), into Active Appearance Models, in order to model face appearances under variable illumination. The proposed algorithm successfully outperforms the traditional linear PCA transform to capture the salient features generated by different illuminations, and reconstruct the illuminated faces with high accuracy. We also consider the problem of automatically classifying human face poses from face views with varying illumination, as well as occlusion and noise. Based on the sparse representation methods, we propose two dictionary-learning frameworks for this pose classification problem. The first framework is the Adaptive Sparse Representation pose Classification (ASRC). It trains the dictionary via a linear model called Incremental Principal Component Analysis (Incremental PCA), tending to decrease the intra-class redundancy which may affect the classification performance, while keeping the extra-class redundancy which is critical for sparse representation. The other proposed work is the Dictionary-Learning Sparse Representation model (DLSR) that learns the dictionary with the aim of coinciding with the classification criterion. This training goal is achieved by the K-SVD algorithm. In a series of experiments, we show the performance of the two dictionary-learning methods which are respectively based on a linear transform and a sparse representation model. Besides, we propose a novel Dictionary Learning framework for Illumination Normalization (DL-IN). DL-IN based on sparse representation in terms of coupled dictionaries. The dictionary pairs are jointly optimized from normally illuminated and irregularly illuminated face image pairs. We further utilize a Gaussian Mixture Model (GMM) to enhance the framework's capability of modeling data under complex distribution. The GMM adapt each model to a part of the samples and then fuse them together. Experimental results demonstrate the effectiveness of the sparsity as a prior for patch-based illumination normalization for face images.

臺灣婦女生育率下降快速，近年來屢創新低，堪稱全球生育率最低的國家，總生育率自民國89年1.68、降為民國98年1.03，民國99年甚至降至0.90以下，提升生育率成為政府施政的重要課題。因為資料限制，生育率大多以總生育率(Total Fertility Rate)表示，而非較能反映婦女一生生育總數的世代完成生育率(Completed Cohort Fertility Rate)。這兩者間存有不少差異，以生育率下降的臺灣為例，總生育率會因生育時機遞延而低估世代生育率，以總生育率詮釋生育率可能有瑕疵。有鑒於此，本文以比較「世代」及「年代」兩者的差異，以生育率及死亡率為研究對象，探討較適宜描述臺灣特性的模型。 由於世代生育率會有資料不足的問題，本文使用外推法(Extrapolation)補足年齡較高（如35歲以上）的婦女生育率，並以四種模型估計年代生育率與世代生育率，包括Gamma模型、Gompertz模型、主成份分析(Principle Component Analysis)與單一年齡組個別估計法，希望找出適合預測臺灣世代完成生育率的模型。除了台灣資料，也用日本、法國與美國的世代生育率資料，比較各國世代生育率模型的異同。另外，本文也以世代及年代兩種觀點，類似生育率的探討方式，比較常用死亡率模型的優劣。 不論是生育率或是死亡率資料，配適模型結果皆以世代資料可得到較好的估計結果，生育率以單一年齡組個別估計法為最佳的模型，死亡率則以Gamma模型、主成份分析、單一年齡組個別估計法為較佳的模型。 / Taiwan’s fertility rates have been declining radically in recent years, much faster than most countries in the world. For example, the total fertility rate (TFR) is 1.68 in 2000, 1.03 in 2009, and even reduces to 0.90 in 2010. Therefore, one of the top priorities for Taiwan government policies is to enhance the willingness of having children. Due to the data availability, the TFR is used more often, although the completed cohort fertility rate (CFR) is a more reasonable measurement. However, previous studies showed that the TFR is likely to be influenced by the deferring (i.e., tempo effect) of childbearing and produces misleading results. In order to measure the effect of deferring childbearing, this study focuses on exploring the difference of measures in the view of cohort and period (especially the CFR vs. TFR) and evaluates which fertility and mortality model is more appropriate for Taiwan. Because there are fewer complete cohort fertility data, we use extrapolation to make up the higher age-group fertility data (such as aged 35 and above). We consider four fertility models in this study, including Gamma model, Gompertz model, principal component analysis, and individual group estimation. We use the data from Taiwan, Japan, France and United State data to evaluate these fertility models. The results indicate that the parametric models (Gamma and Gompertz) have the worst performance, probably due to the rapid change of fertility behaviors. In addition, similar to evaluating the fertility models, we compare the performance of frequently used mortality models using the cohort and period mortality data. The result shows that using cohort data to estimate fertility and mortality is better than period data. Also individual group estimation is the best model to fit fertility; the better models to fit mortality are Gamma model, principle component analysis and individual group estimation.

The objective of this work was to understand microbial population density and diversity, both spatially and temporally, in wetland mesocosms to gain a better fundamental understanding for use in the optimization and design of constructed wetlands (CWs). A standardized community level physiological profiling (CLPP) data analysis protocol was adapted and utilized for CW mesocosms. A new one-dimensional metric was developed to track community divergence using BIOLOGTM ECO plate data. The method proved easy to use, did not require a background in multivariate statistics, and accurately described community divergence in mesocosm systems. To study mesocosm biofilm-bound bacterial communities an appropriate detachment protocol was required. Various shaking protocols were evaluated for their effectiveness in the detachment of bacteria from mesocosm pea gravel, with a focus on detachment of viable and representative bacterial communities. A protocol based on mechanical shaking with buffer and enzymes was identified as an optimal approach and used further in this study. The bacterial communities associated with the interstitial water, pea gravel media, and rhizospheric regions from both planted and unplanted CW mesocosms were profiled using the CLPP method and compared. Vertical community stratification was observed for all mesocosm systems. Rhizospheric communities were found to be significantly more active than their gravel-associated counterparts, suggesting that although rhizospheric bacteria were less abundant in the mesocosms they may play a more significant role in the removal and fate of water born contaminants. The start-up dynamics of CW mesocosms was investigated using the CLPP and standard CW characterization methods over an eight month period. All mesocosms showed a steep increase in interstitial community divergence until day 75-100, at which point a steady-state was reached. The interstitial communities were also characterized in terms of similarity based on experimental design treatments (planted/unplanted and origin of seeding inoculum). Four stages were identified during the start-up consisting of an initial stage where mesocosm communities were differentiated based on origin of the inoculum, a period where adjustments and shifts occurred in all mesocosm, a time where all mesocosm communities were quite similar, and a final state where community differentiations were made based plant presence in the mesocosms.