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The purpose of this study was to compare power output and ventilatory measurements between the steep ramp test (SR) and both the 30-second Wingate anaerobic (WAT) and standard cardiopulmonary exercise tests (CPET) in chronic obstructive pulmonary disease (COPD). 11 patients (7 males and 4 females) underwent spirometry, a CPET, WAT and SR test. Repeated measures ANOVA was used to compare the differences between the peak work rate of the CPET (CPET_peak), SR (SR_peak), and the average power of the WAT (W_avg). The W_avg was higher than the SR_peak, which was higher than the CPET (231.2 ± 113.4, 156.8 ± 67.9, 65.9 ± 35.9, p>0.05 respectively). There were no differences found between the tests at end-exercise for inspiratory reserve volume (IRV), ventilation (V_E), and end-expiratory lung volume (EELV). Tidal volume (V_T) was also compared between the tests as a percentage of the inspiratory capacity (IC) remaining at end-exercise and no differences were found. The similarity between the ventilatory measures indicates a similar level of constraint, despite the large difference in work rates achieved, in all 3 tests. This shows that a standard CPET underestimates leg power in COPD patients, and the WAT and SR may be better indicators of leg muscle power and anaerobic type exercise.

Obesity and its subsequent disease states are major health problems in the United States. In many ways, obesity can be considered a “disease state” itself due to the changes it causes on the body. High-intensity exercise also places acute stress the body, putting humans in recovery from exercise in a state that may be analogous to a temporary disease state. The purpose of this study was to examine biomarkers associated with obesity (CRP and Leptin) before and after continuous and intermittent bouts of exercise in an obese but otherwise healthy sample vs. a healthy, non-obese sample. This investigation focused on examining the obese sample’s biomarkers at rest compared to those of the healthy group immediately and 1 hour-post exercise. Eighteen male subjects participated, with nine in each group. Each subject performed a VO2 max test and a series of three anaerobic Wingate tests at least one week apart in a cross-over study design. Blood was taken at baseline, immediately-post, and 1-hour post for each exercise mode. A significant difference was noted between groups for CRP at baseline on the VO2 testing day. A significant difference between groups existed in leptin levels at baseline on both testing days. The only significant change was the decrease in leptin from post to 1- hour post for during the VO2 in the obese group. However, both exercise protocols demonstrated various effects on the subjects and groups. Healthy participants were examined individually, and two of them showed possible signs of being at risk for obesity and its subsequent disease states based on post exercise “spikes” in CRP and leptin that caused the levels of the biomarkers to be closer to those in the obese group at rest. Another three subjects saw at least two spikes. Thus, a total of five subjects could potentially be “at-risk” based on the assumptions of the present study. These results suggest CRP and Leptin could potentially hold the ability to classify someone in a “preobesity state.” Further investigations are warranted based on these initial results and should focus on biomarkers more specific to obesity.

The purpose of this study was to compare power output and ventilatory measurements between the steep ramp test (SR) and both the 30-second Wingate anaerobic (WAT) and standard cardiopulmonary exercise tests (CPET) in chronic obstructive pulmonary disease (COPD). 11 patients (7 males and 4 females) underwent spirometry, a CPET, WAT and SR test. Repeated measures ANOVA was used to compare the differences between the peak work rate of the CPET (CPET_peak), SR (SR_peak), and the average power of the WAT (W_avg). The W_avg was higher than the SR_peak, which was higher than the CPET (231.2 ± 113.4, 156.8 ± 67.9, 65.9 ± 35.9, p>0.05 respectively). There were no differences found between the tests at end-exercise for inspiratory reserve volume (IRV), ventilation (V_E), and end-expiratory lung volume (EELV). Tidal volume (V_T) was also compared between the tests as a percentage of the inspiratory capacity (IC) remaining at end-exercise and no differences were found. The similarity between the ventilatory measures indicates a similar level of constraint, despite the large difference in work rates achieved, in all 3 tests. This shows that a standard CPET underestimates leg power in COPD patients, and the WAT and SR may be better indicators of leg muscle power and anaerobic type exercise.

Online mining the maximal frequent itemsets over data streams is an important problem in data mining. The maximal frequent itemset is the itemset which the support is large or equal to the minimal support and the itemset is not the subset or superse of each itemset. Previous algorithms to mine the maximal frequent itemsets in the traditional database are not suitable for data streams. Because data streams have some characteristics: (1) continuous (2) fast (3) no data limit (4) real time (5) searching once, mining data streams have many new challenges. First, they are unrealistic to keep the entire stream in the main memory or even in a secondary storage area, since a data stream comes continuously and the amount of data is unbounded. Second, traditional methods of mining on stored datasets by multiple scans are infeasible, since the streaming data is passed only once. Third, mining streams requires fast, real-time processing in order to keep up with the high data arrival rate and mining results are expected to be available within short response time. In order to solve mining maximal frequent itemsets from data streams using the landmark window model, Mao et. al. propose the INSTANT algorithm. In the landmark window model, knowledge discovery is performed based on the values between the beginning time and the present. The advantage of using the landmark window model is that the results are correct as compared to the other models. The structure of the INSTANT algorithm is simple and it can save many memory space. But it takes long time in mining the maximal frequent itemsets. When the new transactions comes, the number of comparisons between the old transactions of INSATNT algorithm is too much. In this thesis, we propose the Set-Checking algorithm to mine frequent itemsets from data streams using the landmark window model. We use the structure of lattice to store our information. The structure of lattice records the subset relationship between the child node and the father node. For every node, we can record the itemset and the support. When the new transaction comes, we consider five relations: (1) equivalent (2) superset (3) subset (4) intersection (5) empty relations. According to the lattice structure of the five sets , we can add the transaction and the renew support efficiently. From our simulation result, we find that the process time of our Set-Checking algorithm is faster than that of the INSTANT algorithm.

Online mining association rules in data streams is an important field in the data mining. Among them, mining the maximal frequent itemsets is also an important issue. A frequent itemset is called maximal if it is not a subset of any other frequent itemset. The set of all the maximal frequent itemsets is denoted as the maximal frequent itemset. Because data streams are continuous, high speed, unbounded, and real time. As a result, we can only scan once for the data streams. Therefore, the previous algorithms to mine the maximal frequent itemsets in the traditional databases are not suitable for the data streams. Furthermore, many applications are interested in the recent data streams, and the sliding window is the model which deal with the most recent data streams. In the sliding window model, a window size is required. One of the algorithms for mining the maximal frequent itemsets based on the sliding window model is called the MFIoSSW algorithm. The MFIoSSW algorithm uses a compact structure to mine the maximal frequent itemsets. It uses an array-based structure A to store the maximal frequent itemsets and other helpful itemsets. But it takes long time to mine the maximal frequent itemsets. When the new transaction comes, the number of comparison between the new transaction and the old transactions is too much. Therefore, in this project, we propose a sliding window approach, the Subset-Lattice algorithm. We use the lattice structure to store the information of the transactions. The structure of the lattice stores the relationship between the child node and the father node. In each node, we record the itemset and the support. When the new transaction comes, we consider five relations: (1) equivalent, (2) subset, (3) intersection, (4) empty set, (5) superset. With this five relations, we can add the new transactions and update the support efficiently.

¡@¡@The research on collection or reflection of solitary waves mainly focus on numerical model and theoretical analytics, there are few study on experiment. due to the process on reaction of solitary waves are very short in times, and the waveform is also hardly to measure quantifiable. ¡@¡@The method present in this paper that we setup a high speed camera at a fixed position, and a grid-point board is located in the water tank and out of the tank after pictured, then we capture the process on reflection of solitary wave at a wall by high speed camera, so that the waveform and the grid surface coincide. finally, we analyze the waveform within the grid by using image techniques. ¡@¡@The results of this paper that present several important parameters in several relative wave height, such as maximal run-up, residual time, phase shift..et.al. the other hand, this paper compare the result of experiment with available evidences likes numerical model and theoretical analytics that found to be in quantitative agreement. ¡@¡@In addition, this paper also present the result of experiment that could compare with the new phenomenon "residual falling jet¡¨, it`s published by Chambarel.et.al (2009) numerical model.

Mining association rules, means a process of nontrivial extraction of implicit, previously and potentially useful information from data in databases. Mining maximal large itemsets is a further work of mining association rules, which aims to find the set of all subsets of large (frequent) itemsets that could be representative of all large itemsets. Previous algorithms to mining maximal large itemsets can be classified into two approaches: exhausted and shortcut. The shortcut approach could generate smaller number of candidate itemsets than the exhausted approach, resulting in better performance in terms of time and storage space. On the other hand, when updates to the transaction databases occur, one possible approach is to re-run the mining algorithm on the whole database. The other approach is incremental mining, which aims for efficient maintenance of discovered association rules without re-running the mining algorithms. However, previous algorithms for mining maximal large itemsets based on the shortcut approach can not support incremental mining for mining maximal large itemsets. While the algorithms for incremental mining, {it e.g.}, the SWF algorithm, could not efficiently support mining maximal large itemsets, since it is based on the exhausted approach. Therefore, in this thesis, we focus on the design of an algorithm which could provide good performance for both mining maximal itemsets and incremental mining. Based on some observations, for example, ``{it if an itemset is large, all its subsets must be large; therefore, those subsets need not to be examined further}", we propose a Sliding-Window approach, the SWMax algorithm, for efficiently mining maximal large itemsets and incremental mining. Our SWMax algorithm is a two-passes partition-based approach. We will find all candidate 1-itemsets ($C_1$), candidate 3-itemsets ($C_3$), large 1-itemsets ($L_1$), and large 3-itemsets ($L_3$) in the first pass. We generate the virtual maximal large itemsets after the first pass. Then, we use $L_1$ to generate $C_2$, use $L_3$ to generate $C_4$, use $C_4$ to generate $C_5$, until there is no $C_k$ generated. In the second pass, we use the virtual maximal large itemsets to prune $C_k$, and decide the maximal large itemsets. For incremental mining, we consider two cases: (1) data insertion, (2) data deletion. Both in Case 1 and Case 2, if an itemset with size equal to 1 is not large in the original database, it could not be found in the updated database based on the SWF algorithm. That is, a missing case could occur in the incremental mining process of the SWF algorithm, because the SWF algorithm only keeps the $C_2$ information. While our SWMax algorithm could support incremental mining correctly, since $C_1$ and $C_3$ are maintained in our algorithm. We generate some synthetic databases to simulate the real transaction databases in our simulation. From our simulation, the results show that our SWMax algorithm could generate fewer number of candidates and needs less time than the SWF algorithm.

In this work, copula models for fitting bivariate response data with Weibull marginal distributions are studied, which are motivated by the need of model fading channels in signal applications. The analytical expressions for the joint probability density function (p.d.f.), and joint cumulative distribution function (c.d.f.) are utilized as the bivariate distribution of the fading channels data with not necessarily identical fading parameters and average powers. The performances of outage probability employing diversity receivers, called as selection combining (SC), equal-gain combining (EGC), and maximal-ratio combining (MRC) of two diversity receivers under bivariate copula models with Weibull marginal distributions are presented. They are also compared with the results in Sagias (2005) where the data assumed to follow the bivariate Weibull distribution. It will be demonstrated that the copula models can approximate the bivariate Weibull distribution used in Sagias (2005) very closely with suitable copula model, and the computations for obtaining the performances of outage probability under SC are much simplified. Keywords and phrases: equal-gain combining, maximal-ratio combining, selection combining

Nanoemulsions with extremely small droplet sizes (<100 nm) have shown several advantages over conventional emulsions. However, almost all nanoemulsions in usage are liquids that restrict their use in many soft materials. The aim of this thesis is to understand the formation and long-term stability of viscoelastic nanogels developed from liquid nanoemulsions. At first, gelation in 40 wt% canola oil-in-water nanoemulsions were investigated as a function of emulsifier type (anionic sodium dodecyl sulfate (SDS) or nonionic Tween 20) and concentration. Three different regimes of colloidal interactions were observed as a function of SDS concentration. 1) At low SDS concentration (0.5 – 2 times CMC) the counterion shell layer increased the effective volume fraction of the dispersed phase (eff) close to the random jamming, resulting in repulsive gelation. 2) At SDS concentration between 5 – 15 times CMC, micelle induced depletion attractions led to extensive droplet aggregation and gelation. 3) At very high SDS concentration, however, oscillatory structural forces (OSF) due to layered-structuring of excess micelles in the interdroplet regions led to loss of gelation. In repulsive gelation, reduction in droplet size coupled with the electrical double layer resulted in a linear increase of Gʹ. On the contrary, attractive nanoemulsions showed rapid increase in gel strength below a critical droplet radius, and was explained by transformation of OSF into depletion attraction. No gelation was seen in Tween 20 nanoemulsions, due to lack of repulsive interactions and weak depletion attraction. Next the influence of the dispersed phase volume fraction () on repulsive nanoemulsion gelation was investigated and the Gʹ values were modeled using empirical scaling law developed by Mason et al. (1995). It was found that an initial liquid regime transformed into glassy phase at a eff = g ~ 0.58, where droplets are entrapped in a cage of neighbouring droplets due to crowding. It was followed by jamming transition at a critical volume fraction (j), where droplet deformation led to large increase in elasticity. The model predicted j = 0.7, which is close to the predictions for repulsive polydispersed emulsions found in the literature. In the final phase long-term stability of the nanogels was evaluated until 90 days, during which the nanogels remained stable to creaming and coalescence. However, repulsive nanogels showed a significant decrease in Gʹ and the gels converted into flowable liquids over time. For attractive nanogels decrease in Gʹ was much less, although given enough time they would also transformed into weak gels. It was hypothesized that surface active compounds generated due to lipid oxidation altered interfacial charge cloud leading to loss of gel strength for repulsive nanogels. For attractive nanogels slippery bonds in the aggregates permitted rotational and translational diffusion of nanodroplets on the surface of each other leading to network compactness and a decrease in gel strength with time. Overall, it was concluded that it is possible to form nanogels from canola oil nanoemulsions using ionic emulsifiers. The gel strength and stability of the nanogels depends on emulsifier concentration, droplet size,  and the chemical stability of the oil used. More investigation is needed in order to improve the long-term stability of the nanogels. The nanogels possess high potential for use in low-fat foods, pharmaceuticals, and cosmetic products.