Effects of active and passive lacebacks on antero-posterior position of maxillary first molars and central incisors

The purpose of this study was to compare the effects of active and passive lacebacks on antero-posterior position of maxillary first molars and central incisors during leveling phase. Twenty-three subjects with Class I and Class II malocclusion were treated with first premolars extraction using preadjusted appliances (MBT 0.022-inch brackets). The leveling phase was performed with stainless steel archwires only. The sample was divided into 2 groups: 14 subjects received active lacebacks (Group 1) and 9 subjects received passive lacebacks (Group 2). Lacebacks were made from 0.008-inch ligature wire. Lateral cephalometric radiographs were taken pre- and post-leveling phase. Student's t-test was applied to determine the differences between pre- and post-leveling mean values and to determine the mean differences between groups. In Group I, the first molars showed a significant mesial movement, whereas no change was observed in Group 2. In both groups, maxillary central incisor crowns moved to lingual side. In conclusion, active laceback produced anchorage loss of maxillary first molars whereas passive laceback did not affect the position of these teeth. Active and passive lacebacks were effective in preventing central incisor proclination.

A new snowfall detection algorithm for high latitude regions based on a combination of active and passive sensors

Precipitation retrieval over high latitudes, particularly snowfall retrieval over ice and snow, using satellite-based passive microwave spectrometers, is currently an unsolved problem. The challenge results from the large variability of microwave emissivity spectra for snow and ice surfaces, which can mimic, to some degree, the spectral characteristics of snowfall. This work focuses on the investigation of a new snowfall detection algorithm specific for high latitude regions, based on a combination of active and passive sensors able to discriminate between snowing and non snowing areas. The space-borne Cloud Profiling Radar (on CloudSat), the Advanced Microwave Sensor units A and B (on NOAA-16) and the infrared spectrometer MODIS (on AQUA) have been co-located for 365 days, from October 1st 2006 to September 30th, 2007. CloudSat products have been used as truth to calibrate and validate all the proposed algorithms. The methodological approach followed can be summarised into two different steps. In a first step, an empirical search for a threshold, aimed at discriminating the case of no snow, was performed, following Kongoli et al. [2003]. This single-channel approach has not produced appropriate results, a more statistically sound approach was attempted. Two different techniques, which allow to compute the probability above and below a Brightness Temperature (BT) threshold, have been used on the available data. The first technique is based upon a Logistic Distribution to represent the probability of Snow given the predictors. The second technique, defined Bayesian Multivariate Binary Predictor (BMBP), is a fully Bayesian technique not requiring any hypothesis on the shape of the probabilistic model (such as for instance the Logistic), which only requires the estimation of the BT thresholds. The results obtained show that both methods proposed are able to discriminate snowing and non snowing condition over the Polar regions with a probability of correct detection larger than 0.5, highlighting the importance of a multispectral approach.

Global scale investigation of cirrus clouds properties using active and passive sensors

A year of satellite-borne lidar CALIOP data is analyzed and statistics on occurrence and distribution of bulk properties of cirri are provided. The relationship between environmental and cloud physical parameters and the shape of the backscatter profile (BSP) is investigated. It is found that CALIOP BSP is mainly affected by cloud geometrical thickness while only minor impacts can be attributed to other quantities such as optical depth or temperature. To fit mean BSPs as functions of geometrical thickness and position within the cloud layer, polynomial functions are provided. It is demonstrated that, under realistic hypotheses, the mean BSP is linearly proportional to the IWC profile. The IWC parameterization is included into the RT-RET retrieval algorithm, that is exploited to analyze infrared radiance measurements in presence of cirrus clouds during the ECOWAR field campaign. Retrieved microphysical and optical properties of the observed cloud are used as input parameters in a forward RT simulation run over the 100-1100 cm-1 spectral interval and compared with interferometric data to test the ability of the current single scattering properties database of ice crystal to reproduce realistic optical features. Finally a global scale investigation of cirrus clouds is performed by developing a collocation algorithm that exploits satellite data from multiple sensors (AIRS, CALIOP, MODIS). The resulting data set is utilized to test a new infrared hyperspectral retrieval algorithm. Retrieval products are compared to data and in particular the cloud top height (CTH) product is considered for this purpose. A better agreement of the retrieval with the CALIOP CTH than MODIS is found, even if some cases of underestimation and overestimation are observed.

The independent role of prenatal and postnatal exposure to active and passive smoking on the development of early wheeze in children.

Maternal smoking during pregnancy increases childhood asthma risk, but health effects in children of nonsmoking mothers passively exposed to tobacco smoke during pregnancy are unclear. We examined the association of maternal passive smoking during pregnancy and wheeze in children aged ≤2 years.Individual data of 27 993 mother-child pairs from 15 European birth cohorts were combined in pooled analyses taking into consideration potential confounders.Children with maternal exposure to passive smoking during pregnancy and no other smoking exposure were more likely to develop wheeze up to the age of 2 years (OR 1.11, 95% CI 1.03-1.20) compared with unexposed children. Risk of wheeze was further increased by children's postnatal passive smoke exposure in addition to their mothers' passive exposure during pregnancy (OR 1.29, 95% CI 1.19-1.40) and highest in children with both sources of passive exposure and mothers who smoked actively during pregnancy (OR 1.73, 95% CI 1.59-1.88). Risk of wheeze associated with tobacco smoke exposure was higher in children with an allergic versus nonallergic family history.Maternal passive smoking exposure during pregnancy is an independent risk factor for wheeze in children up to the age of 2 years. Pregnant females should avoid active and passive exposure to tobacco smoke for the benefit of their children's health.

Application of active and passive suspension techniques to improve high speed ground vehicle performance - phase II. Final report.

Transportation Department, Office of University Research, Washington, D.C.

Active and passive immunization against oral Candida albicans infection in a murine model

Background/aims: Clinical and laboratory studies are consistent with a major role for cell-mediated immunity in recovery from oral infection with Candida albicans, but the role of humoral immunity remains controversial. The purpose of this study was to establish the relative contributions of cellular and humoral immunity to protection against oral candidiasis in a murine model, and to determine whether host responses could be enhanced by different immunization strategies. Results: Active oral immunization was protective in BALB/c and CBA/CaH mice, reducing both fungal burden and duration of infection after secondary challenge, whereas systemic immunization failed to protect against subsequent oral challenge. Candida-specific IgM was the predominant antibody detected in serum following both primary and secondary oral challenge; however, Candida-specific salivary IgA was not detectable. Immunization by passive transfer of either lymphocytes or immune serum did not confer any significant protection against oral infection in either susceptible or resistant mouse strain. Conclusion: The data demonstrate a possible role for mucosa-associated immunity following active immunization by the oral route, most likely exerted by local T lymphocytes resident in the oral mucosa, but there was no evidence to support a role for humoral immunity in protection against oral candidiasis.

Role of active and passive recovery in adaptations to high intensity training

It has been established that Wingate-based high-intensity training (HIT) consisting of 4 to 6 x 30-s all-out sprints interspersed with 4-min recovery is an effective training paradigm. Despite the increased utilisation of Wingate-based HIT to bring about training adaptations, the majority of previous studies have been conducted over a relatively short timeframe (2 to 6 weeks). However, activity during recovery period, intervention duration or sprint length have been overlooked. In study 1, the dose response of recovery intensity on performance during typical Wingate-based HIT (4 x 30-s cycle all-out sprints separated by 4-min recovery) was examined and active recovery (cycling at 20 to 40% of V̇O2peak) has been shown to improve sprint performance with successive sprints by 6 to 12% compared to passive recovery (remained still), while increasing aerobic contribution to sprint performance by ~15%. In the following study, 5 to 7% greater endurance performance adaptations were achieved with active recovery (40%V̇O2peak) following 2 weeks of Wingate-based HIT. In the final study, shorter sprint protocol (4 to 6 x 15-s sprints interspersed with 2 min of recovery) has been shown to be as effective as typical 30-s Wingate-based HIT in improving cardiorespiratory function and endurance performance over 9 weeks with the improvements in V̇O2peak being completed within 3 weeks, whereas exercise capacity (time to exhaustion) being increased throughout 9 weeks. In conclusion, the studies demonstrate that active recovery at 40% V̇O2peak significantly enhances endurance adaptations to HIT. Further, the duration of the sprint does not seem to be a driving factor in the magnitude of change with 15 sec sprints providing similar adaptations to 30 sec sprints. Taken together, this suggests that the arrangement of recovery mode should be considered to ensure maximal adaptation to HIT, and the practicality of the training would be enhanced via the reduction in sprint duration without diminishing overall training adaptations.

Quantum error correction with the IBM quantum experience

Mentre si svolgono operazioni su dei qubit, possono avvenire vari errori, modificando così l’informazione da essi contenuta. La Quantum Error Correction costruisce algoritmi che permettono di tollerare questi errori e proteggere l’informazione che si sta elaborando. Questa tesi si focalizza sui codici a 3 qubit, che possono correggere un errore di tipo bit-flip o un errore di tipo phase-flip. Più precisamente, all’interno di questi algoritmi, l’attenzione è posta sulla procedura di encoding, che punta a proteggere meglio dagli errori l’informazione contenuta da un qubit, e la syndrome measurement, che specifica su quale qubit è avvenuto un errore senza alterare lo stato del sistema. Inoltre, sfruttando la procedura della syndrome measurement, è stata stimata la probabilità di errore di tipo bit-flip e phase-flip su un qubit attraverso l’utilizzo della IBM quantum experience.

Iterative Phase Optimization of Elementary Quantum Error Correcting Codes

Performing experiments on small-scale quantum computers is certainly a challenging endeavor. Many parameters need to be optimized to achieve high-fidelity operations. This can be done efficiently for operations acting on single qubits, as errors can be fully characterized. For multiqubit operations, though, this is no longer the case, as in the most general case, analyzing the effect of the operation on the system requires a full state tomography for which resources scale exponentially with the system size. Furthermore, in recent experiments, additional electronic levels beyond the two-level system encoding the qubit have been used to enhance the capabilities of quantum-information processors, which additionally increases the number of parameters that need to be controlled. For the optimization of the experimental system for a given task (e.g., a quantum algorithm), one has to find a satisfactory error model and also efficient observables to estimate the parameters of the model. In this manuscript, we demonstrate a method to optimize the encoding procedure for a small quantum error correction code in the presence of unknown but constant phase shifts. The method, which we implement here on a small-scale linear ion-trap quantum computer, is readily applicable to other AMO platforms for quantum-information processing.

Vector Error-Correction Models in a consumer packaged goods category forecasting decision support system

A framework for developing marketing category management decision support systems (DSS) based upon the Bayesian Vector Autoregressive (BVAR) model is extended. Since the BVAR model is vulnerable to permanent and temporary shifts in purchasing patterns over time, a form that can correct for the shifts and still provide the other advantages of the BVAR is a Bayesian Vector Error-Correction Model (BVECM). We present the mechanics of extending the DSS to move from a BVAR model to the BVECM model for the category management problem. Several additional iterative steps are required in the DSS to allow the decision maker to arrive at the best forecast possible. The revised marketing DSS framework and model fitting procedures are described. Validation is conducted on a sample problem.

Development of error correction techniques for nitrate-N load estimation models

Excess nutrient loads carried by streams and rivers are a great concern for environmental resource managers. In agricultural regions, excess loads are transported downstream to receiving water bodies, potentially causing algal blooms, which could lead to numerous ecological problems. To better understand nutrient load transport, and to develop appropriate water management plans, it is important to have accurate estimates of annual nutrient loads. This study used a Monte Carlo sub-sampling method and error-corrected statistical models to estimate annual nitrate-N loads from two watersheds in central Illinois. The performance of three load estimation methods (the seven-parameter log-linear model, the ratio estimator, and the flow-weighted averaging estimator) applied at one-, two-, four-, six-, and eight-week sampling frequencies were compared. Five error correction techniques; the existing composite method, and four new error correction techniques developed in this study; were applied to each combination of sampling frequency and load estimation method. On average, the most accurate error reduction technique, (proportional rectangular) resulted in 15% and 30% more accurate load estimates when compared to the most accurate uncorrected load estimation method (ratio estimator) for the two watersheds. Using error correction methods, it is possible to design more cost-effective monitoring plans by achieving the same load estimation accuracy with fewer observations. Finally, the optimum combinations of monitoring threshold and sampling frequency that minimizes the number of samples required to achieve specified levels of accuracy in load estimation were determined. For one- to three-weeks sampling frequencies, combined threshold/fixed-interval monitoring approaches produced the best outcomes, while fixed-interval-only approaches produced the most accurate results for four- to eight-weeks sampling frequencies.

Active versus passive management : the case of BOVESPA

Mestrado em Finanças