This study was undertaken to evaluate gender differences in predictors of substance use in clinic-referred, Hispanic American adolescents with substance use disorders. Individual (disruptive behavior disorders, depression) and family variables (family conflict, parental monitoring) were evaluated as predictors of the initial level and change over time in marijuana use, and gender was evaluated as a moderator of the associations. The study involved an analysis of an existing dataset of 113 Hispanic American adolescents (93 boys; age 12 to 17) referred for outpatient treatment for substance abuse and their parental guardian. Participants and parental guardians completed questionnaires and a structured interview to report on predictor variables at baseline and marijuana use at baseline and 3-, 6-, 12-, and 18-months post-baseline. Latent growth curve modeling was conducted to evaluate the study hypotheses. Adolescents reported high levels of marijuana use at baseline and relatively stable levels of marijuana use over time. Treatment and gender effects influenced the marijuana use trajectory. Girls exhibited more impaired psychosocial functioning than boys, including worse disruptive behavior problems and depression and lower levels of parental monitoring. Depression was negatively associated with marijuana use longitudinally. Overall, individual and family risk factors are associated with adolescent marijuana use in complex ways. Implications for intervention are discussed.

The purpose of this study was to evaluate variations of the Natural Resources Conservation Service (NRCS) curve number (CN) method for estimating near real-time runoff for naturalized flow, using high resolution radar rainfall data for watersheds in various agro-climatic regions of Texas. The CN method is an empirical method for calculating surface runoff which has been tested on various systems over a period of several years. Many of the findings of previous studies indicate the need to develop variations of this method to account for regional and seasonal changes in weather patterns and land cover that might affect runoff. This study seeks to address these issues, as well as the inherent spatial variability of rainfall, in order to develop a means of predicting runoff in near real-time for water resource management. In the past, raingauge networks have provided data for hydrologic models. However, these networks are generally unable to provide data in real-time or capture the spatial variability associated with rainfall. Radar networks, such as the Next Generation Weather Radar (NEXRAD) of the National Weather Service (NWS), which are widely available and continue to improve in quality and resolution, can accomplish these tasks. In general, a statistical comparison of the raingauge and NEXRAD data, where both were available, shows that the radar data is as representative of observed rainfall as raingauge data. In this study, watersheds of mostly homogenous land cover and naturalized flow were used as study areas. Findings indicate that the use of a dry antecedent moisture condition CN value and an initial abstraction (Ia) coefficient of 0.1 produced statistically significant results for eight out of the ten watersheds tested. The urban watershed used in this study produced more significant results with the use of the traditional 0.2 Ia coefficient. The predicted results before and during the growing season, in general, more closely agreed with the observed runoff than those after the growing season. The overall results can be further improved by altering the CN values to account for seasonal vegetation changes, conducting field verification of land cover condition, and using bias-corrected NEXRAD rainfall data.

In the past the machinery used in punching of flexible printed circuit boards(FPCBs), it used mostly the rotary motor as the power source in the mechanism design. To transfer rotary motion to linear motion need a succession of mechanical conversion components, in order to achieve the purpose of linear output. However these mechanical parts for transforming bring some unavoidable problems such as the machinery itself huge volume, backlash and friction which created during the action process, all have harmful influences on the system dynamic performance and precision. Voice coil actuator has direct-drive output, high response and high thrust force these characteristics, therefore this research apply voice coil actuator to the punching of flexible printed circuit boards. For present industry, S-curve velocity profile is often used in point-to-point displacement intermittent action applications, due to its jerk-limited characteristic for reducing vibration and raising precision. Then integrating plans of S-curve velocity profile with voice coil actuator based on punching characters, to analyze the whole system dynamic performance in such a vertical linear output application. Then generalizing the dependence of influence factors of punching quality and motion characteristics of punching mechanism through experimental results. The achievement of this research could provide references for some related designers using similar linear actuators in vertical linear output applications.

In the age of Web2.0, various network services became critical. Exchange of messages between entities in the network is so frequent that information explosion is quite common nowadays. Volume of Information passed is growing up rapidly. With the wide development of web applications, people need to learn how to filter the important messages; service providers have urgent need to trace the ever changing role of users. This research studies the detections of the user interaction scenario, based on the result from the test function in the on-line learning platform. The learning platform users are divided into two groups, teachers and students, based on their roles. Usually students sit for an on-line examination at the end of each learning activity. The teachers are in charge of helping students with their presentations, encouraging those with good grades, and helping the weaker ones to reach their potential. But in the one-to-many teaching method, a teacher needs to face many students and the resultant grade of an examination becomes a heap of fuzzy and difficult to comprehend numbers. Even though some mathematical tools can help the teachers analyze the data, it is still very difficult to provide appropriate response to each student. The purpose of this research motives building an examination system which combines context-awareness and auto-notification, and bring the advantages of digital examination. An inference engine is used to calculate linear regression of learning curve for each student, then review the old data, and transfer the analysis into the learning context. Then the feedback is given to the students under the various learning context or the teacher will get notification after it compile the analysis. Besides analyzing the past data, the linear regression result will be adjusted to fit the characteristics of learning curve and infer the personal goal of the student. If result is better than expected goal, students should be encouraged. On the other hand, the remediable actions will be administered. Those events can be scheduled by the manager of auto-notification system, published in the appropriate time, and achieve the goal of variety, personalization, and automation.

In elliptic curve cryptography, cryptosystems are based on an additive subgroup of an elliptic curve defined over a finite field, and the hardness of the Elliptic Curve Discrete Logarithm Problem is dependent on the order of this subgroup. In particular, we often want to find a subgroup with large prime order. Hence when finding a suitable curve for cryptography, counting the number of points on the curve is an essential step in determining its security. In 1985, René Schoof proposed the first deterministic polynomial-time algorithm for point counting on elliptic curves over finite fields. The algorithm was improved by Noam Elkies and Oliver Atkin, resulting in an algorithm which is sufficiently fast for practical purposes. The enhancements leveraged the arithmetic properties of the l-th classical modular polynomial, where l- is either an Elkies or Atkin prime. As the Match-Sort algorithm relating to Atkin primes runs in exponential time, it is eschewed in common practice. In this thesis, I will discuss my implementation of the Schoof-Elkies-Atkin algorithm in C++, which makes use of the NTL package. The implementation also supports the computation of classical modular polynomials via isogeny volcanoes, based on the methods proposed recently by Bröker, Lauter and Sutherland. Existing complexity analysis of the Schoof-Elkies-Atkin algorithm focuses on its asymptotic performance. As such, there is no estimate of the actual impact of the Match-Sort algorithm on the running time of the Schoof-Elkies-Atkin algorithm for elliptic curves defined over prime fields of cryptographic sizes. I will provide rudimentary estimates for the largest Elkies or Atkin prime used, and discuss the variants of the Schoof-Elkies-Atkin algorithm using their run-time performances. The running times of the SEA variants supports the use Atkin primes for prime fields of sizes up to 256 bits. At this size, the selective use of Atkin primes runs in half the time of the Elkies-only variant on average. This suggests that Atkin primes should be used in point counting on elliptic curves of cryptographic sizes.

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Due to the fast development in 3D technology during recent decades, many approaches in 3D representation technologies have been proposed worldwide. In order to get an accurate information to render a 3D representation, more data need to be recorded compared to normal video sequence. In this case, how to find an efficient way to transmit the 3D representation data becomes an important part in the whole 3D representation technology. Recent years, many coding schemes based on the principle of encoding the depth have been proposed. Compared to the traditional multiview coding schemes, those new proposed schemes can achieve higher compression efficiency. Due to the development of depth capturing technology, the accuracy and quality of the reconstructed depth image also get improved. In this thesis we propose an efficient depth data compression scheme for 3D images. Our proposed depth data compression scheme is platelet based coding using Lagrangian optimization, quadtree decomposition and quadratic curve fitting. We study and improve the original platelet based coding scheme and achieve a compression improvement of 1-2 dB compared to the original platelet based scheme. The experimental results illustrate the improvement provided by our scheme. The quality of the reconstructed results of our proposed curve fitting based platelet coding scheme are better than that of the original scheme.

The effect of temperature on predator and herbivore consumption is an important factor for predicting the effects of climate warming on ecosystems. The Metabolic Theory of Ecology (MTE) describes the temperature dependence of biological and ecological rates and states that metabolism is the fundamental biological mechanism that governs most observed patterns in ecology. This statement has been criticized empirically for a number of organismal traits and systematic deviations have been found. Here, a meta-analysis is performed on published temperature responses of ectotherm consumption. The mean effect of temperature on consumption was higher than the mean value predicted by proponents of the MTE and was highly variable. Some of this variation is explained by habitat type, where the consumption rates of marine organisms displayed stronger temperature dependence than for terrestrial and freshwater organisms. The frequency distribution of temperature dependencies is right skewed for consumption. Here, this skewness is explained by a methodological artefact as values close to “no effect” are more unlikely to be sampled than others when fitting the Arrhenius equation. In conclusion, the assumptions of the MTE do not hold for rates of consumption and marine organisms display a stronger temperature dependence compared to terrestrial and freshwater organisms.

Elliptic curve cryptosystem is one type of public-key system, and it can guarantee the same security level with Rivest, Shamir and Adleman (RSA) with a smaller key size. Therefore, the key of elliptic curve cryptography (ECC) can be more compact, and it brings many advantages such as circuit area, memory requirement, power consumption, performance and bandwidth. However, compared to private key system, like Advanced Encryption Standard (AES), ECC is still much more complicated and computationally intensive. In some real applications, people usually combine private-key system with public-key system to achieve high performance. The ultimate goal of this research is to architect a high performance ECC processor for high performance applications such as network server and cellular sites.<p> In this thesis, a high performance processor for ECC over Galois field (GF)(2^163) by using polynomial presentation is proposed for high-performance applications. It has three finite field (FF) reduced instruction set computer (RISC) cores and a main controller to achieve instruction-level parallelism (ILP) with pipeline so that the largely parallelized algorithm for elliptic curve point multiplication (PM) can be well suited on this platform. Instructions for combined FF operation are proposed to decrease clock cycles in the instruction set. The interconnection among three FF cores and the main controller is obtained by analyzing the data dependency in the parallelized algorithm. Five-stage pipeline is employed in this architecture. Finally, the u-code executed on these three FF cores is manually optimized to save clock cycles. The proposed design can reach 185 MHz with 20; 807 slices when implemented on Xilinx XC4VLX80 FPGA device and 263 MHz with 217,904 gates when synthesized with TSMC .18um CMOS technology. The implementation of the proposed architecture can complete one ECC PM in 1428 cycles, and is 1.3 times faster than the current fastest implementation over GF(2^163) reported in literature while consumes only 14:6% less area on the same FPGA device.