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The present study experimentally investigated college students’ perceived message hurtfulness (PMH) associated with a parent’s pessimistic message about their future. The originality of this study is its 2 x 4 x 3 factorial design, which included 24 different combinations of self-perception and parental pessimistic messages as the independent variables and PMH as the dependent variable. Four hundred eighty-four participants completed the online, cross-sectional survey. They were randomly assigned to a writing exercise for priming the self (best vs. worst possible self) and its domain (subjective well-being, personal relationship, career, and general). After the priming exercises, participants were randomly assigned to read one of three hypothetical situations involving a child’s receiving a father’s pessimistic comment on the future (a child’s future on their subjective well-being, relationship, and career). After reading, participants assessed the degree of PMH. ANOVA and multiple regression were used for data analysis. Neither analysis elicited statistically significant results at the .05 level for the influences of self-priming factors on PMH (H1) or domain homogeneity of PMH (H2). Although the manipulation did not work, this study found that biological sex was the most significant factor of PMH at the .05 level. More research is needed to further explore biological sex to better understand the differences between children’s sex and PMH in a parent-child relationship.

Statistical shape analysis has progressed significantly over the last few decades, especially with the increasing availability of large datasets and with computational advances. Shape has been widely used as a primary feature for detection, tracking and recognition of objects in images and videos. Analyzing shapes of curves (outlines of objects in 2D images) or surfaces (outlines of objects in 3D images) has been of great interest in diverse applicationsranging from medical imaging to computer vision and beyond. Shape analysis makes use of ideas from geometry and statistics. Specifically, much work has been devoted to the development of appropriate representations and metrics for shape data based on conceptsfrom Riemannian geometry.Statistical shape data have three main properties: (1) shape is invariant to shape-preserving transformations: translation, scaling, rotation, and re-parameterization, (2) shapes are inherently infinite dimensional with strong dependence among the position of nearby points, and (3) shape spaces are not Euclidean, but rather are fundamentally curved. Thus, one generally cannot directly apply standard statistical tools developed for multivariate data to shape data. To accommodate these features of the data, we work with a special representation of shape, called the square-root velocity function (SRVF), to provide a useful formal description of the shape. In addition, we use the elastic metric which enables efficient statistical analysis on the corresponding shape space under the SRVF representation.The main focus of this dissertation is classification of shapes. While many statistical frameworks have been developed for the classification problem, most are strongly tied to early formulations of the problem – with an object to be classified described as a vector in a relatively low-dimensional Euclidean space. Thus, we first linearize the shape space by passing to tangent spaces of the manifold of shapes at different projection points, and then use principal components within these tangent spaces to reduce dimensionality. We propose a novel method using multiple projection points which effectively separate shapes forpairwise classification. We aggregate these pairwise classifiers for the multiclass problem. We consider classification procedures based on linear and quadratic discriminant analysis (LDA and QDA) in the lower dimensional linearized spaces. We use two different final decision rules for classification based on aggregated likelihoods from the LDA or QDA models: one-shot and recursion. We illustrate the impact of the projection point and choice of subspace on the misclassification rate through extensive simulation studies and empirical real data examples. We further apply our methods to other types of data including data on spheres, landmark shape data, and nonelastic shape data. We also compare our methods to other classification approaches including distance-based methods (nearest neighbor and nearest mean classifiers), classwise models, and alternative approaches for dimension reduction.Next, we develop an effective graphical tool that plays off the histogram for shapes and functions, two types of data which are known to pose challenges for visualization. We use various choices of score to order shapes, such as the first tangent principal component coefficient, residual of fitted shapes to a particular tPC space, and geometric features (average curvature and area for closed curves). The shapes are then binned on the basis of this score. They are displayed, with the raw data appearing on the top of the plot and fitted shapes on the bottom. For functional data, we can visualize the amplitude component on the top and the phase component on the bottom. If a covariate is available, we can incorporate it in the graphical display. These visualization techniques are proving to be useful exploratory tools to guide a formal analysis. Third, we propose a method of canonical correlation analysis (CCA) for probability density functions (PDFs) and shapes. Based on the elastic shape analysis framework, we use separate tangent spaces defined at the mean of two groups and use an eigen basis estimated from the covariance to reduce the dimension. This tangent CCA approach using a low dimensional representation has the advantage of easier computation and interpretation, compared to other functional CCA approaches. These tools are especially useful in discovering associations between PDFs of intensities for quantifying tumor heterogeneity and tumor shapes, in a medical imaging application. These associations then help characterize the disease via non-invasive imaging.

This dissertation study takes a holistic approach to the act of task performance, investigating how task and learner variables independently and interactively influence task performance and task experience. Drawing on Robinson’s (2001b) triadic componential framework for task design, the study explores the task demands that are inherent to tasks (task complexity) or that emerge in task–learner interaction (task difficulty). Task complexity and modality were adopted as task feature variables, while goal orientation (Dweck, 1986), the L2 motivational self system (Dörnyei, 2009b), and L2 anxiety were adopted as learner variables. Task performance was examined in terms of complexity, accuracy, and fluency (CAF), and task experience was represented by flow, conditioned by optimal challenge (Csikszentmihalyi, 1987). With the mediation of task difficulty, this study examines the relative contributions of task and learner variables to task performance and task experience. In a repeated-measures design, 141 L2 learners performed four argumentative tasks that differed in task complexity and modality. They completed two questionnaires, one a pre-task survey examining dispositional motivation and the other a post-task survey probing their subjective experience with the task. Production data were analyzed to measure complexity (i.e., the clause per T-unit ratio), accuracy (i.e., the error-free clause per T-unit ratio), and fluency (i.e., pause frequency per T-unit). MANOVAs, correlation analysis, and structural equation modeling (SEM) were conducted. The results indicate that although task complexity affected the perception of task difficulty, it did not affect task performance in terms of CAF nor task experience of flow. Task modality significantly predicted task performance and task experience of flow. Significant relationships among motivational constructs were observed, but learner variables did not have a direct impact on immediate task performance or experience. These findings suggest that task variables have a stronger impact than learner variables on both language learning outcomes and affective outcomes of tasks. The study’s multifaceted investigation provides insight into how various motivational and affective constructs work together and interact with various task features to produce unique task performance experiences and performance outcomes.

Capacitorless dynamic random access memory (DRAM) is a promising solution to cell-area scalability and complex fabrication process issues for conventional DRAM. The thin body SOI transistor, which suppresses the short channel effect and also minimizes variability, is selected for the capacitorless DRAM cell structure. The impact of substrate doping concentration on capacitorless DRAM cell performance is studied and a novel selective well structure is proposed. A capacitorless DRAM cell design with BJT-based operation (BJT mode) is known to have larger sensing margins and longer retention times. Controlling band-to-band tunneling leakage (BTBT) related to the electric field plays a key role in limiting retention time. In the BJT mode, BTBT in the Hold 0 state limits data retention time (D0 failure). By optimizing the underlap between the front gate and the source/drain regions as well as the operating voltages, retention time exceeding 1 second should be attainable for a cell with 25 nm gate length. The scaling limits of optimized capacitorless DRAM cells are also investigated through the analysis of variations. Signal sense margin analysis indicates that the ultimate scaling limit is 13 nm (gate length) for embedded DRAM applications and 16.5 nm for stand-alone DRAM applications. The positive feedback MOSFET (PF-FET) was fabricated on thin body (10 nm) and UTBOX (10 nm) SOI structure. Positive feedback occurs as a result of both the BJT operation and the floating body effect from weak impact ionization. It exhibits very steep subthreshold slope of 0.03 mV/dec. Wide hysteresis enables the PF-FET to be utilized for memory application. The sensing margin is 62 µA/µm and retention time is greater than 4 seconds.

Wireless sensor networks (WSNs) have been deployed for environment monitoring and surveillance. A message delivery service is one of the most fundamental services for WSNs, thus making its efficiency and effectiveness important. A widely-adopted protocol for message delivery in WSNs is a geographic forward routing (GFR), in which messages are greedily forwarded to their destinations. In this thesis, we develop network services complementary to the existing GFR for efficient and secure message delivery in WSNs. We first develop a distributed location service protocol (DLSP) for message delivery to mobile nodes. Since GFR represents destinations of messages with destinations' geographic locations, the knowledge of location of mobile nodes is necessary to ensure correct message delivery. In DLSP, mobile nodes select some sensor nodes as their location servers, and publish the mobiles' location information to the location servers. Sensor nodes contact those location servers to retrieve the current location of mobile nodes when needed. DLSP provides systematic methods for mobile nodes to select location servers and publish their location to those servers, and for sensor nodes to query mobiles' location. We then design an algorithm called Traverse for hole boundary detection and geographic forward routing with hole avoidance (GFRHA) for efficient message routing. Traverse identifies boundaries of holes, i.e., areas without any functioning sensor node. GFRHA then utilizes the identified hole information to route messages around holes while being forwarded before they encounter holes. This way, the message path lengths, and subsequently the message delay and energy consumption, can be significantly reduced, depending on hole shapes and source and destination locations. We also develop attack-resilient collaborative message authentication (ARCMA) for message delivery. ARCMA is designed to tolerate node-capture attacks, in which attackers obtain valid keys by compromising physically-exposed sensor nodes, and use the keys to generate forged messages. To defend against such attacks, in ARCMA, messages are collaboratively authenticated by a set of sensor nodes rather than by one node. The security of ARCMA does not degrade unless attackers simultaneously compromise more than a certain number of sensor nodes.

The manuscripts from the Ilkhanate (1258-1370), the Iranian part of the Mongol Empire, contain paintings that evoke an illusion of three-dimensional space and passage of time for the first time in the Islamic world. They also show signs of modeling, wash, and calligraphic strokes and include scrolling clouds, rolling hills, and sinuous tree trunks, techniques and motifs that were absent in pre-Ilkhanid Islamic arts and that seem to originate in Chinese landscape paintings. This new way of rendering land was one of the most significant stylistic changes that occurred in the Ilkhanid paintings. The dissertation focuses on the representations of nature in the Jami al-Tawarikh ("Compendium of Chronicles" 1306-7), the world history written by vizier Rashid al-Din. It considers an epistemological analogy between the additive pictorial space in the Jami al-Tawarikh paintings and the layered and interpolated historical spaces in the text of the Jami al-Tawarikh. Rashid al-Din centers the Islamic world on the Ilkhanid land space, which is contrastingly called "a far distant corner" in the Yuan Shi. The political dialectic between center and periphery may have been visualized in some of the Jami al-Tawarikh paintings, in which the representations of nature dominate the center while the protagonists are relegated to the sides. In the Jami al-Tawarikh paintings, the visual elements seem to move at various velocities; while some clouds curl tightly, hills may move up and down at a lower frequency, and the figures may seem rooted firmly on the ground. These movements evoke a sense of differently-paced times, not unlike the scientist Nasir al-Din al-Tusi's (1201-1274) theorization of motion and time in the treatise, Tadhkira fi ‘ilm al-hay’a ("A Memoir on the Science of Astronomy"), that they are perceived quantities that depend on the reference point of the observer. The stylistic changes in the Ilkhanid paintings were so drastic that it is considered that the tradition of Persian painting began in the Ilkhanid period. The renditions of land in the Jami al-Tawarikh paintings may have newly provided a visual language parallel and correlative to the verbal one as a means of conceptualizing space, time, and memory.

The numerical simulation of arc welding process provides insight and information not available from experiments for process development, but has not been used in practical welding applications. In order to demonstrate its usefulness in welding applications, a three-dimensional numerical simulation of the pulsed gas metal arc welding (P-GMAW) process using Volume of Fluid technique was developed based on mathematical models. It was validated by the comparison of weld deposit geometry, transient radius, and temperature history. The physical mechanism of weld bead hump formation, which has not been clearly understood, and a suppression technique were explored based on heat and fluid flow profiles and solid/liquid interface contours obtained from the numerical simulation of P-GMAW and hybrid (P-GMAW + laser) processes. The mechanism of hump formation was investigated by using the numerical simulation. According to simulation results of P-GMAW, traveling at a high travel speed, the events leading to the formation of a humped bead were identified. In the initial stage of hump formation, a thin, elongated, molten bead was formed and then pinched due to capillary instability, resulting in a dramatically reduced cross section of the molten weld bead. Solidification then divided the weld pool into front and back sections, guaranteeing hump formation. The numerical simulation was also used to demonstrate the suppression of hump formation by hybrid process. Simulation results of hybrid process showed that a defocused laser beam located in front of the P-GMA weld pool could suppress hump formation. A shallow “skin” melt produced by the defocused laser beam, with sufficient beam intensity and beam radius, promoted a wider weld bead with a smaller internal contact angle, which was less susceptible to capillary instability of weld metal deposit.

The purpose of this document is to examine selected piano compositions by Robert Muczynski and to trace the important impact of the tradition of Russian composer-pianists from the middle nineteenth century through the early twentieth century with a discussion of stylistic features. The document is intended to offer pianists and piano teachers an introduction to the Six Preludes, Op. 6 and the Toccata, Op. 15. Robert Muczynski (b.1929) is an American composer-pianist trained under the Russian composer-pianist Alexander Tcherepnin (1899-1977). Muczynski was strongly influenced by his teacher’s Russian heritage and career as a composer-pianist. Tcherepnin passed on to Muczynski not only compositional techniques but a model for a career that combines composition and performance following a tradition that dates back to Anton Rubinstein (1829-1894), a founder of the Russian piano school. These Russian influences on Muczynski have greatly contributed to his style and to his career as a composer-pianist so that his career has come to resemble that of a Russian pianist. Muczynski’s Six Preludes, Op. 6, and Toccata, Op. 15, include many stylistic features that represent the composer’s profound knowledge of the instrument and of performance. They offer excellent material for teaching late intermediate and early advanced piano students because the musical ideas in each piece are clear and each piece isolates a specific technical challenge. Chapter 1 presents a biographical sketch of the composer and a survey of his works for solo piano. Chapter 2 traces the performance traditions and the stylistic origins of the Russian piano school. Chapter 3 examines stylistic features of Muczynski’s music through comparison with features found in the works of Tcherepnin and Prokofiev. The performer’s guide to Six Preludes, Op. 6, and to Toccata, Op. 15, in Chapter 4 is intended as a point of departure for the student at the intermediate or advanced level. The conclusion summarizes the current study and conclu des that Muczynski crystallized the essence of Russian tradition in his works which benefit from the composer’s extensive understanding of pianism at the top level as a composer- pianist.

The phenomenon of business failure has attracted research interest in finance literature partly because of its impact on the U.S economy. Whereas an impressive body of knowledge has been accumulated on this subject thus far, the hospitality literature has lacked empirical studies that seek to explain the nature of this phenomenon in the hospitality industry. The restaurant industry has consistently had the most business failures of any single segment within the retail trade sector in the eighties. Therefore, there were three purposes in this study: 1) to develop a model for predicting business failure which can be a useful tool in helping researchers and industry practitioners to identify warning signs of business failure in the restaurant industry, 2) to determine whether the financial variables of a predictive model for business failure in the restaurant industry are the same as in the hotel industry, and 3) to determine whether the financial variables that are associated with reorganization are different from those that are associated with liquidation in the restaurant industry. The sample consisted of 23 failed and 23 non-failed restaurant firms, and 15 failed and 15 non-failed hotel firms within the period of 1982-1993. The predictive business failure models were developed through logistic regression analysis employing 8 financial variables based on one year prior to business failure. The models were tested at two and three years prior to business failure. The empirical evidence illustrated that the business failure model developed for the restaurant industry is capable of predicting business failure, and even bankruptcy with high classification accuracy. The relationship between reorganization and liquidation was investigated through logistic regression analysis employing two sets of indicators for capital structure and profitability. The sample consisted of 14 reorganizers and 10 liquidators from the restaurant industry. The empirical evidence showed that reorganization and liquidation are not dependent on each other, that is, reorganization and liquidation cannot be determined by both Capital structure and profitability in the restaurant a failed [end of author-provided abstract].