Massive ice interactions with offshore structures

Thesis (Ph.D.) University of Alaska Fairbanks, 1992The interaction between a multiyear sea ice floe of variable thickness, and an offshore structure, has been examined using a 3-dimensional finite element method. Elastic response within the ice floe was assumed initially, and a uniform loading of the ice floe by the adjacent pack ice was used. As an example of the results for a frozen boundary condition at the ice/structure contact zone, with a central region of the ice floe having its thickness reduced to 50% as compared to the floe thickness at the structure ($\Delta$t/t = 0.5), tensile cracks first form at the top surface in the thinnest area of the floe. The total force on the structure was 108 MN, as compared with 1500 MN which would be present in the case of an ice floe of uniform thickness. Parameters varied were ice/structure contact zone (located in the centric or the eccentric region), the sliding boundary condition, two-dimensional ice thickness variation, variable ice elastic modulus as a function of depth, and viscoelastic ice behavior. Cases of rigid and of compliant structure and foundation were included. In a second part of the study, the ice island loads acting upon a cylindrical rigid structure were analyzed by this 3-dimensional finite element method. A force of 6600 MN was computed to be acting on the structure with a maximum penetration distance of 8.2 m. A different theoretical method based upon multiyear ice field data resulted in a force of 336 MN and a maximum penetration distance of 75 m. The ice forces on the structure are reduced by ice floe thickness variations, and also are affected by the geometries at the ice floe/structure and ice floe/pack ice boundaries. The reduced elastic modulus in the warmer. lower part of an ice sheet promotes ice bending failure and causes lower structure loads

Developing and evaluating a compassion-based therapy for trauma-related shame and posttraumatic stress

Posttraumatic Stress Disorder (PTSD) has been primarily conceptualized as a fear-based disorder, but accumulating research indicates that shame can also strongly contribute to the development and maintenance of PTSD. Existing evidence-based treatments for PTSD typically focus on dysregulated fear responding and do not directly target the affective experience of shame. Interventions that promote self-compassion have shown promise for reducing shame related to various clinical problems, but this approach has not been systematically evaluated in traumatized individuals. The aim of this study was to develop and evaluate a brief compassion-based therapy, with the hypothesis that it would reduce trauma-related shame and PTSD symptoms. The intervention consisted of six weekly individual therapy sessions focused on promoting self-compassion in response to a traumatic event and its sequelae. Using a multiple baseline design, the intervention was evaluated in a community sample of trauma-exposed adults (N = 10) with elevated shame and PTSD symptoms. Participants completed assessments on a weekly basis during a 2-, 4-, or 6-week baseline phase and 6-week treatment phase, and at 2- and 4-weeks after the intervention. By the end of treatment, 90% of participants demonstrated reliable decreases in PTSD symptom severity (p < .05), while 80% of participants showed reliable reductions in shame (p < .05), relative to their respective scores at baseline. These improvements were maintained at 2- and 4-week follow-up, with large effect sizes for PTSD symptom severity (d = 2.26) and shame (d = 2.12), compared to scores at baseline. The intervention was also associated with improvements in self-blame (d = 2.61), self-compassion (d = 2.28), mindfulness (d = 2.21), positive affect (d = 1.07), and negative affect (d = 2.14). Greater increases in self-compassion from baseline to follow-up were correlated with greater reductions in PTSD symptom severity (r = -.76, p < .05) and in shame (r = -.79, p < .01). Participants reported high levels of satisfaction with the intervention. The results from the present study support the hypothesis that compassion-based therapy is associated with reductions in trauma-related shame and PTSD symptoms. The marked improvements observed during the relatively brief intervention suggest that the intervention may be useful as either a stand-alone treatment or as a supplement to other treatments

Hidden Markov Models for Pipeline Damage Detection Using Piezoelectric Transducers

Oil and gas pipeline leakages lead to not only enormous economic loss but also environmental disasters. How to detect the pipeline damages including leakages and cracks has attracted much research attention. One of the promising leakage detection method is to use lead zirconate titanate (PZT) transducers to detect the negative pressure wave when leakage occurs. PZT transducers can generate and detect guided stress waves for crack detection also. However, the negative pressure waves or guided stress waves may not be easily detected with environmental interference, e.g., the oil and gas pipelines in offshore environment. In this paper, a Gaussian mixture model based hidden Markov model (GMM-HMM) method is proposed to detect the pipeline leakage and crack depth in changing environment and time-varying operational conditions. Leakages in different sections or crack depths are considered as different states in hidden Markov models (HMM). Laboratory experiments show that the GMM-HMM method can recognize the crack depth and leakage of pipeline such as whether there is a leakage, where the leakage is

A Comparison between Markov Switching Zero-inflated and Hurdle Models for Spatio-temporal Infectious Disease Counts

In epidemiological studies, zero-inflated and hurdle models are commonly used to handle excess zeros in reported infectious disease cases. However, they can not model the persistence (from presence to presence) and reemergence (from absence to presence) of a disease separately. Covariates can sometimes have different effects on the reemergence and persistence of a disease. Recently, a zero-inflated Markov switching negative binomial model was proposed to accommodate this issue. We present a Markov switching negative binomial hurdle model as a competitor of that approach, as hurdle models are often also used as alternatives to zero-inflated models for accommodating excess zeroes. We begin the comparison by inspecting the underlying assumptions made by both models. Hurdle models assume perfect detection of the disease cases while zero-inflated models implicitly assume the case counts can be under-reported, thus we investigate when a negative binomial distribution can approximate the true distribution of reported counts. A comparison of the fit of the two types of Markov switching models is undertaken on chikungunya cases across the neighborhoods of Rio de Janeiro. We find that, among the fitted models, the Markov switching negative binomial zero-inflated model produces the best predictions and both Markov switching models produce remarkably better predictions than more traditional negative binomial hurdle and zero-inflated models

Mitigating Greenhouse Gas Emissions from Winter Production of Agricultural Greenhouses

Consuming conventional fossil fuel, such as coal, natural gas, and oil, to heat agricultural greenhouses has contributed to the climate change and air pollutions regionally and globally, so the clean energy sources have been increasingly applied to replace fossil energies in heating agricultural greenhouses, especially in urban area. To assess the environment performance (e.g., greenhouse gas (GHG) emissions) of the ground source heat pump system (GSHPs) for heating agricultural greenhouses in urban area, a GSHPs using the shallow geothermal energy (SGE) in groundwater was applied to heat a Chinese solar greenhouse (G1) and a multispan greenhouse (G2) in Beijing (latitude 39°40′ N), the capital city of China. Emission rates of the GSHPs for heating the G1 and G2 were quantified to be 0.257–0.879 g CO2 eq. m−2 day−1. The total GHG emissions from heating greenhouses in Beijing with the GSHPs were quantified as 1.7–2.9 Gt CO2 eq. year−1 based on the electricity from the coal-fired power plant (CFPP) and the gas-fired power plant (GFPP). Among different stages of the SGE flow, the SGE promotion contributed most GHG emissions (66%) in total due to the higher consumption of electricity in compressors. The total GHG emissions from greenhouses heating with the coal-fired heating system (CFHs) and gas-fired heating system (GFHs) were quantified as 2.3–5.2 Gt CO2 eq. year−1 in Beijing. Heating the G1 and G2 with the GSHPs powered by the electricity from the CFPP, the equivalent CO2 emissions were 43% and 44% lower than directly burning coal with the CFHs but were 46% and 44% higher than the GFHs that burn natural gas. However, when using the GFPP-generated electricity to run the GSHPs, the equivalent CO2 emissions would be 84% and 47% lower than the CFHs and the GFHs, respectively

Effects of Comparative Metabolism on Tomato Fruit Quality under Different Levels of Root Restriction

In a soilless culture (perlite substrate), root restriction cannot only reduce production costs but also improve fruit quality. Therefore, this study used different levels of root restriction [T1: 0.5 L, T2: 4 L, nonrestriction treatment (CK): 35 L] on tomatoes to explore their impact on quality. Results showed that total soluble solids (TSS), glucose, fructose, and sucrose contents were increased, whereas L-tryptophan, L-tyrosine, and titratable acidity were decreased under two restriction treatments. Meanwhile, root restriction also promoted the accumulation of phenylalanine and proline. For lycopene and flavonoid biosynthesis (prunin, naringin, naringenin), the restriction groups were significantly higher than those in the control group. Overall, T1 and T2 treatment had a better effect than CK treatment. This study provided an idea for improving substrate use efficiency and tomato quality

Applications of functional near-infrared spectroscopy in non-drug therapy of traditional Chinese medicine: a review

Non-drug therapies of traditional Chinese medicine (TCM), including acupuncture, massage, tai chi chuan, and Baduanjin, have emerged as widespread interventions for the treatment of various diseases in clinical practice. In recent years, preliminary studies on the mechanisms of non-drug therapies of TCM have been mostly based on functional near-infrared spectroscopy (fNIRS) technology. FNIRS is an innovative, non-invasive tool to monitor hemodynamic changes in the cerebral cortex. Our review included clinical research conducted over the last 10 years, establishing fNIRS as a reliable and stable neuroimaging technique. This review explores new applications of this technology in the field of neuroscience. First, we summarize the working principles of fNIRS. We then present preventive research on the use of fNIRS in healthy individuals and therapeutic research on patients undergoing non-drug therapies of TCM. Finally, we emphasize the potential for encouraging future advancements in fNIRS studies to establish a theoretical framework for research in related fields

Combination Forecasting Method of Ship Maintenance Cost Based on Integrated Weighting

In view of the existing combination forecasting methods based on rough set theory that may not be able to weight individual individual forecasting models, the attribute importance in the original method is adjusted and combined with the root mean square error of the individual forecasting model to form a new attribute importance. The new attribute importance is used to determine the combination forecasting weight coefficients, which solves the problem that the original method cannot be weighted, and increases the consideration of forecasting accuracy. Weight coefficients are also determined according to the historical forecasting performance of the models, which reflects the forecasting stability of the models. The integrated weighting method is used to fuse the two kinds of weight coefficients. Based on a certain type of ship maintenance cost data example, the improved method is compared with the commonly used combined forecasting methods, and better results are obtained, the accuracy and stability of the forecasting are improved, and the effectiveness of the method is verified