COMPARABLE GROWTH AND PRODUCTIVITY OF I. aquatica ON HYDROPONIC SUBSYSTEMS WITHIN AQUAPONIC SYSTEM

The growth and productivity of two Water Spinach varieties (Kankung Unggul BikaR and Kangkung Bangkok LP-1R) had already been experiment on two different Hydroponic culture subsystems (Floating Raft and Pumice Bed) of an Aquaponic system, where mineral nutrients of the Water Spinach vegetables were absorbed from bio-chemical processes in solid wastes of fish and excess feeds. Consequently, this study showed individual functions of these two different Hydroponic subsystems from new established dates no significant differences on the 7 date of testing. Until increasingly significant difference for the Hydroponic subsystem of Floating Raft is less effective than Hydroponic subsystem of Pumice Bed based on the length of shoot, length of petiole, width of leaf on the 14 test date and the length of shoot, length of petiole, length of leaf, the number of leaves and length of root on the 21 test date. Nonetheless, Hydroponic subsystem did not support the growth and productivity of the Water Spinach varieties in all stages of testing about statistical significance. In addition, in term of productivity and growth criteria on the 7, 14 and 21 dates of testing, no significant difference was observed between two Water Spinach varieties. Finally, the advice of this study does not reassess the treatment (Floating Raft Hydroponic subsystem and Kangkung Unggu BikaR variety) because of the poor result of length of shoot, length of petiole and reality of yield for this Kangkung Unggul BikaR variety are probably at the 21 date

Multi-Stage Statistical Models for Cancer in Observational Studies and SMARTs

Many diseases, especially cancer, are not static, but rather can be summarized by a series of events or stages (e.g. diagnosis, remission, recurrence, metastasis, death). Most available methods to analyze multi-stage data ignore intermediate events and focus on the terminal event or consider (time to) multiple events as independent. Competing-risk or semi-competing-risk models are often deficient in describing the complex relationship between disease progression events that are driven by a shared progression stochastic process. In the first chapter, we propose a semi-parametric joint model of diagnosis, latent metastasis, and cancer death and use nonparametric maximum likelihood to estimate covariate effects on the risks of intermediate events and death and the dependence between them. We illustrate the model using SEER prostate cancer data. In the second chapter, we focus on the adverse effect of younger diagnosis age on cancer survival. We use a joint model with a shared gamma frailty term to interpret the effect as a consequence of correlation between diagnosis time and the post-diagnosis survival time. In the traditional analysis, diagnosis time is treated as the time origin for a model of overall survival that fails to utilize the full information leading up to diagnosis. Often the available covariates do not fully explain the correlation between time-to-diagnosis and time-to-death calling for use of joint modeling and frailties to extend the model. We show that the variance of the frailty term and covariate effects can be estimated by a nonparametric maximum likelihood method. Laplace transformation is used to derive likelihood contributions. The model is applied to Michigan SEER breast cancer data. In the third chapter, we compare dynamic treatment regimens from clinical trials with multiple rounds of treatment randomization (sequential multiple assignment randomized trials, SMARTs). Previously proposed methods to analyze data with survival outcomes from a SMART use inverse probability weighting and provide non-parametric estimation of survival rates, but no other information. We apply a joint modeling approach here to provide unbiased survival estimates and as a mechanism to include auxiliary covariates, treatment effects and their interaction within regimens. We address the multiple comparisons problem using multiple-comparisons-with-the-best (MCB).PHDBiostatisticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/136967/1/quitran_1.pd

Interactive mixed reality media with real time 3D human capture

Master'sMASTER OF ENGINEERIN

Thermogravimetry and neutron thermodiffractometry studies of the H-YBa2Cu3O7 system.

The high Tc superconducting oxide YBa2Cu3O7¿x reacts with hydrogen gas. Thermogravimetric, X-ray and neutron scattering experiments allow us to propose a two-step type of hydrogen bonding. Firstly, a few hydrogen atoms fill some oxygen vacancies and may favourably modify the electron state, giving rise to a slight increase in the critical temperature. Secondly, after a prolonged heating period, the collapse of the YBa2Cu3O7¿x type framework and of superconductivity were observed, and a new, highly hydrogenated material appeared

A questão do suicídio em O som e a fúria

Trata-se de uma tradução, e a versão original não possui resumo

Applications of Mach-Zehnder Interferometry to Studies on Local Deformation of Polymers Under Photocuring

A Mach‐Zehnder interferometer (MZI) was built and modified to in situ monitor the deformation of polymers during the photocuring process. In this review, the working principle and method of operation of this MZI were explained together with the method of data analysis. As the examples for the utilization of this modified MZI, measurements of the deformation induced by photopolymerization was demonstrated for three different types of samples: homopolymer in the bulk state, miscible polymer blends and phase‐separated polymer blends. Finally, a concluding remark is provided for the usage of MZI in polymer research

Bearing Capacity of Ring Foundations on Anisotropic and Heterogenous Clays: FEA, NGI‑ADP, and MARS

Axisymmetric solutions for the bearing capacity of ring foundation resting on anisotropic and heterogenous clays are presented in this paper using finite element analysis (FEA). The NGI-ADP model in PLAXIS FEA, a widely used anisotropic soil model, is adopted to study the stability responses of ring foundations, with special consideration given to the effects of increasing undrained shear strength with the depth. Numerical results are formulated in terms of a dimensionless stability number (bearing capacity ratio) that is a function of three dimensionless input parameters: namely, the ratio of inner and outer radius, the increasing strength gradient ratio, and the anisotropic shear strength ratio. The influence of each dimensionless input parameter on the bearing capacity ratio is investigated using design charts and failure mechanisms, and they are scored by relative importance indexes in multivariate adaptive regression splines (MARS) model—a machine learning approach. A highly accurate equation generated from the MARS model is proposed as an effective tool for engineering practitioners

A Machine Learning-Assisted Numerical Predictor for Compressive Strength of Geopolymer Concrete Based on Experimental Data and Sensitivity Analysis

Geopolymer concrete offers a favourable alternative to conventional Portland concrete due to its reduced embodied carbon dioxide (CO2) content. Engineering properties of geopolymer concrete, such as compressive strength, are commonly characterised based on experimental practices requiring large volumes of raw materials, time for sample preparation, and costly equipment. To help address this inefficiency, this study proposes machine learning-assisted numerical methods to predict compressive strength of fly ash-based geopolymer (FAGP) concrete. Methods assessed included artificial neural network (ANN), deep neural network (DNN), and deep residual network (ResNet), based on experimentally collected data. Performance of the proposed approaches were evaluated using various statistical measures including R-squared (R2), root mean square error (RMSE), and mean absolute percentage error (MAPE). Sensitivity analysis was carried out to identify effects of the following six input variables on the compressive strength of FAGP concrete: sodium hydroxide/sodium silicate ratio, fly ash/aggregate ratio, alkali activator/fly ash ratio, concentration of sodium hydroxide, curing time, and temperature. Fly ash/aggregate ratio was found to significantly affect compressive strength of FAGP concrete. Results obtained indicate that the proposed approaches offer reliable methods for FAGP design and optimisation. Of note was ResNet, which demonstrated the highest R2 and lowest RMSE and MAPE values

A mixed indium–iron lithium diphosphate, In0.51Fe0.49LiP2O7

The structure of In0.51Fe0.49LiP2O7 consists of a three-dimensional network constructed from (InIII/FeIII)O6 octa­hedra and P2O7 groups. Each M IIIO6 octa­hedron is linked to six PO4 tetra­hedra belonging to five different P2O7 groups and shares two corners with the same P2O7 group so as to build infinite chains or rather parallel colums of [M IIIP2O11] running along the a axis. The linkage between these chains or columns defines hepta­gonal tunnels parallel to [100] in which the Li+ ions are located in off-centred positions. The In0.51Fe0.49LiP2O7 compound can be regarded as one composition of the continuous solid solution between LiFeP2O7 and LiInP2O7 whose structure is isotypic with the A IFeP2O7 (A I = Na, K, Rb, Cs and Ag) diphosphate family

Prognosis of neonatal tetanus in the modern management era: an observational study in 107 Vietnamese infants.

OBJECTIVES: Most data regarding the prognosis in neonatal tetanus originate from regions where limited resources have historically impeded management. It is not known whether recent improvements in critical care facilities in many low- and middle-income countries have affected indicators of a poor prognosis in neonatal tetanus. We aimed to determine the factors associated with worse outcomes in a Vietnamese hospital with neonatal intensive care facilities. METHODS: Data were collected from 107 cases of neonatal tetanus. Clinical features on admission were analyzed against mortality and a combined endpoint of 'death or prolonged hospital stay'. RESULTS: Multivariable analysis showed that only younger age (odds ratio (OR) for mortality 0.69, 95% confidence interval (CI) 0.48-0.98) and lower weight (OR for mortality 0.06, 95% CI 0.01-0.54) were significantly associated with both the combined endpoint and death. A shorter period of onset (OR 0.94, 95% CI 0.88-0.99), raised white cell count (OR 1.17, 95% CI 1.02-1.35), and time between first symptom and admission (OR 3.77, 95% CI 1.14-12.51) were also indicators of mortality. CONCLUSIONS: Risk factors for a poor outcome in neonatal tetanus in a setting with critical care facilities include younger age, lower weight, delay in admission, and leukocytosis