Most Popular Books by Hoang Nguyen

Twins are born to a noble family -- Mathilde and Giselle, the Sisters Grey. For centuries, the Greys have protected the ruler of Mitteleuropa, but this is a different time -- it is the dawn of the Industrial Age and a great war rages. When the Kaiser is found dead, Giselle is accused of his murder. Pursued by her sisters and hunted by the enemy, Giselle must unravel the prophecy of the Carbon Grey before history itself is rewritten. Collecting the complete CARBON GREY graphic novel series by Hoang Nguyen, Paul Gardner, Khari Evans, and Kinsun Loh, this 304-page omnibus has been re-edited and remastered to tell the entire saga of the Sisters Grey in a new edition designed to open the doors for an expanded universe through tabletop role-playing and other planned supplemental releases.

Nguyen examines mortgage fraud as an inherent part of the subprime mortgage crisis. He traces the exponential growth of mortgage fraud to the loose underwriting standards, alternative loan products, and inadequate regulation and regulatory oversight of the subprime mortgage industry. He describes the various financial crimes constituting mortgage origination fraud, a form of fraud involving fraud for profit, fraud for property, and predatory lending. The accounts of mortgage frauds by industry insiders presented in this book provide a chilling view of the criminal implications of an unregulated financial industry. Nguyen proposes several broad recommendations highlighting the need to recognize the potential for insider fraud, enhance government regulation and oversight, tighten loan qualification requirements, and increase standards of underwriting.

Purpose: To evaluate the changes in retention of pink Locator attachments after exposure to various denture cleansers...

Air pollution is a considerable environmental stressor for urban residents in developing countries. Perceived health risks of air pollution might induce migration intention among inhabitants. The current study employed the Bayesian Mindsponge Framework (BMF) to investigate the rationale behind the domestic and international migration intentions among 475 inhabitants in Hanoi, Vietnam - one of the most polluted capital cities worldwide. We found that people perceiving more negative impacts of air pollution in their daily life are more likely to have migration intentions. The effect of perceived air pollution impact on international migration intention is stronger than that of domestic migration. Acknowledging a family member''s air pollution-induced sickness moderated the association between perceived air pollution impact and domestic migration intention, while the personal experience of air pollution-induced sickness did not. In contrast, the moderation effect of personal experience of sickness became significant in the international migration circumstance, but the effect of information about a family member''s sickness was negligible. The findings suggest that urban inhabitants'' consideration of air pollution averting strategies reflects some characteristics of Homo Oeconomicus . Although an individual''s socio-economic decision may seem insignificant on a collective scale, through environmental stressors as catalysts, such decisions might result in considerable social tendencies (e.g., internal migration and emigration).

In contrast to a current propellant-based rocket, the concept in this thesis uses the rotational mass to create an imbalanced centrifugal force in a non-circular enclosure, which results in a propulsion force being generated. The approach to analyze and validate this imbalance centrifugal force using the simple harmonic motion method through the use of Mathematica software to solve and plot out the cam profile curve, the velocity, the acceleration, the centrifugal force, and the linear momentum. This thesis would be an ideal solution to space propulsion. Furthermore, this concept is lightweight, especially compared to expellants, because the necessary thrust is self-contained, repeated, and reused.

Abstract Fluids in nanopores with diameters of

Honeycomb reactor is often used in pollution abatement applications. A typical commercial honeycomb reactor is made of a ceramic material, which is deposited with the high surface area washcoat carrier such as Al2O3. The polluted gases enter the reactor channels and diffuse through this porous structure to the catalytic sites where they are converted to less harmful gases. Stringent environmental regulations have driven the markets toward increasing engine efficiencies and better after-treatment strategies. Achieving high hydrocarbon conversion requires comprehensive information about the thermal and concentration fields inside the monolith reactor. The concentration fields can be studied by a Spatially Resolved Capillary Inlet Mass Spectrometer technique (Spaci-MS). On the other hand, the temperature field is often measured by positioning thermocouples at various locations inside the monolith channel, which can be intrusive to the reactor and not suitable for fast transient processes. To overcome this limitation, I use a new optoelectronic technique called coherent optical frequency domain reflectometry (c-OFDR). This technique enables instant temperature measurement along a fiber with high spatial and temporal resolutions. The objectives of my works are: i) illustrate the capability of the c-OFDR method to measure transient temperature inside the monolith channel, ii) employed c-OFDR method to analyze the veracity of the concentration measurement by the Spaci-MS probe, iii) develop the experimental setup combining the c-OFDR and Spaci-MS techniques to study the dynamic behaviors of the Pt/CeO2/Al2O3 catalyst system under periodic lean-rich operation.

When developing new techniques to analyze neuro-chemical microenvironments, it is important to realize the incredible variability in the cellular content and the response to stimulation between cells and within a single cell. Conventional analysis techniques yield an average result to describe the content and function of cells. This approach often misses important information since the onset of pathological conditions is always initiated in a small number of cells. New minimally invasive single cell analysis techniques are required for single cell studies in order to gain new insights and understanding of cells2 functions. The objective of my Ph. D. study was to fabricate, characterize, and apply submicrometric fluorescence sensors for the analysis of neuron cells. This dissertation will report the fabrication of miniaturized fluorescence sensors for Ca2+, pH and Zn2+ analysis. In the first approach, liposomes (phospholipid vesicles) were used as miniaturized containers for fluorescent sensing reagents. Liposome-based fluorescence sensing technology offers several advantages over commonly used fluorescence sensing techniques including high spatial resolution, protection of the sensing dye from quenchers and high biocompatibility. However, liposome based sensors were found to be unstable in the cellular environment. The second approach was to synthesize submicrometric particle-based fluorescence sensors named lipobeads to replace the fluorescent liposomes in cellular studies. Lipobeads are polystyrene particles that are coated with a phospholipid membrane. One unique advantage of fluorescent sensing lipobeads is the ability to immobilize hydrophobic indicator molecules in the phospholipid membrane. This enables the use of these indicators in aqueous media since the lipobeads are fully water miscible. The lipobeads also proved to be highly biocompatible in cellular studies. This is attributed to their phospholipid bilayer membrane, which is similar in structure to cell membranes. The dissertation will describe the analytical properties of fluorescence sensing lipobeads and their application in studying zinc ion release and pH changes near neuron cells under physiological conditions, conditions of neuronal injury and stress and acidic cortical spreading depression during stroke like conditions.

Cyber Physical System (CPS) is an integration of an embedded system with network coupled tightly with physical processes and is considered as the next generation of embedded systems. As a CPS needs to respond simultaneously to the changes in the environment that it is monitoring, the computational cost and real-time constraints are the main challenges and important factors when designing an effective CPS. To overcome these challenges, Chip Multi-Processors (CMP) have been used to enhance the overall system performance by exploring the computational parallelism. Nevertheless, the performance of the whole system is still constrained by the overhead of data transfers between sensors/actuators and processors. While CMP just needs about a few microseconds to execute the control algorithm to make a decision, it may need tens of microseconds to copy the dataset among sensors/actuators, external memory, and internal memory of CMP [22]. In this thesis, in order to accelerate the access time between sensors/actuators and processors, we introduce an I/O processing scheme combined with a DMA data prefetching optimization technique. Typically, the I/O data is first placed into main memory which takes many clock cycles to access from processors. In this method, the I/O data is placed directly into SPM which reduces the access time between processor and I/O data. To do this, this approach uses DMA to transfer data between I/O port and on-chip memory. In addition, in this approach, we propose a DMA operation optimization technique to reduce the latency by placing DMA operation initialization on processors'' idle time frames. The experimental results collected by using benchmark programs and graphs generated by TGFF tool clearly show that the proposed approach improves performance of the system significantly. Based on the normalized performance gains using three to five-core CPSs in our experiment results, our approach brings up to 25% improvement on the average in execution time compared to the traditional I/O data transfer scheme without using DMA optimization technique.

The thesis evaluates the benefit of allowing for long memory volatility dynamics in forecasts of the variance-covariance matrix for asset allocation. First, I compare the forecast performance of multivariate long memory conditional volatility models (the long memory EWMA, long memory EWMA-DCC, FIGARCH-DCC and Component GARCH-DCC models) with that of short memory conditional volatility models (the short memory EWMA and GARCH-DCC models), using the asset allocation framework of Engle and Colacito (2006). The research reports two main findings. First, for longer horizon forecasts, long memory volatility models generally produce forecasts of the covariance matrix that are statistically more accurate and informative, and economically more useful than those produced by short memory volatility models. Second, the two parsimonious long memory EWMA models outperform the other models - both short memory and long memory - in a majority of cases across all forecast horizons. These results apply to both low and high dimensional covariance matrices with both low and high correlation assets, and are robust to the choice of estimation window. The research then evaluates the application of multivariate long memory conditional volatility models in dynamic asset allocation, applying the volatility timing procedure of Fleming et al. (2001). The research consistently identifies the economic gains from incorporating long memory volatility dynamics in investment decisions. Investors are willing to pay to switch from the static to the dynamic strategies, and especially from the short memory volatility timing to the long memory volatility timing strategies across both short and long investment horizons. Among the long memory conditional volatility models, the two parsimonious long memory EWMA models, again, generally produce the most superior portfolios. When transaction costs are taken into account, the gains from the daily rebalanced dynamic portfolios deteriorate; however, it is still worth implementing the dynamic strategies at lower rebalancing frequencies. The results are robust to estimation error in expected returns, the choice of risk aversion coefficients and the use of a long-only constraint. To control for estimation error in forecasts of the long memory high dimensional covariance matrix, the research develops a dynamic long memory factor (the Orthogonal Factor Long Memory, or OFLM) model by embedding the univariate long memory EWMA model of Zumbach (2006) into an orthogonal factor structure. The factor-structured OFLM model is evaluated against the six above multivariate conditional volatility models in terms of forecast performance and economic benefits. The results suggest that the OFLM model generally produces impressive forecasts over both short and long forecast horizons. In the volatility timing framework, portfolios constructed with the OFLM model consistently dominate the static and other dynamic volatility timing portfolios in all rebalancing frequencies. Particularly, the outperformance of the factor-structured OFLM model to the fully estimated LM-EWMA model confirms the advantage of the factor structure in reducing estimation error. The factor structure also significantly reduces transaction costs, making the dynamic strategies more feasible in practice. The dynamic factor long memory volatility model also consistently produces more superior portfolios than those produced by the traditional unconditional factor and the dynamic factor short memory volatility models.

Introduction: Acute pesticide poisoning (APP) continues to affect farm workers, especially in low-and middle- income countries (LMIC). The dose-response relationship between exposure and APP is well-researched, but pesticide exposure assessment in a practical environment can be difficult to perform considering various work practices and protections in place. It is well known that inadequate PPE use is a risk factor of APP, but it is unknown which types of inadequate PPE use, such as face (face mask and eye protection) or other types of general protection (such as gloves, boots, and long clothing), is most harmful.Methods: This study aimed to identify if inadequate PPE is an indicator of APP risk following established specifications for meta-analysis of epidemiological studies. Included studies reported an odds ratio (OR) between PPE use to APP in agricultural workers. Data extracted from selected articles included: authors, publication year, country of origin, farm type, population size, method of data collection and time frame of reported symptoms, job task, type of PPE and pesticides used, adjustments made in analysis, OR for APP, and 95% confidence intervals (CI). Meta-analysis was done using a random effects model where ORs were pooled to assess an overall estimate for poisoning odds. Results: Our findings suggested that the inadequate PPE use was associated with increased odds (OR = 1.57, 95% CI=1.16-2.12) of having APP. Failure to use general protection and inadequate face protection increased odds of APP by 1.29 times (95% CI=0.88-1.90) and 1.92 times (95% CI=1.23-3.00), respectively. Conclusions: The meta-analysis results indicate that improper facial protection and general protection are not differently associated with APP odds. Our study concludes that more secured protection against inhalation and dermal contact are critical because any gaps in comprehensive full-body PPE would put workers at APP risk.