ASU Scholarship Showcase

Background:
Ketogenic diets are high fat and low carbohydrate or very low carbohydrate diets, which render high production of ketones upon consumption known as nutritional ketosis (NK). Ketosis is also produced during fasting periods, which is known as fasting ketosis (FK). Recently, the combinations of NK and FK, as well as NK alone, have been used as resources for weight loss management and treatment of epilepsy.

Methods:
A crossover study design was applied to 11 healthy individuals, who maintained moderately sedentary lifestyle, and consumed three types of diet randomly assigned over a three-week period. All participants completed the diets in a randomized and counterbalanced fashion. Each weekly diet protocol included three phases: Phase 1 - A mixed diet with ratio of fat: (carbohydrate + protein) by mass of 0.18 or the equivalence of 29% energy from fat from Day 1 to Day 5. Phase 2- A mixed or a high-fat diet with ratio of fat: (carbohydrate + protein) by mass of approximately 0.18, 1.63, or 3.80 on Day 6 or the equivalence of 29%, 79%, or 90% energy from fat, respectively. Phase 3 - A fasting diet with no calorie intake on Day 7. Caloric intake from diets on Day 1 to Day 6 was equal to each individual’s energy expenditure. On Day 7, ketone buildup from FK was measured.

Results:
A statistically significant effect of Phase 2 (Day 6) diet was found on FK of Day 7, as indicated by repeated analysis of variance (ANOVA), F(2,20) = 6.73, p < 0.0058. Using a Fisher LDS pair-wise comparison, higher significant levels of acetone buildup were found for diets with 79% fat content and 90% fat content vs. 29% fat content (with p = 0.00159**, and 0.04435**, respectively), with no significant difference between diets with 79% fat content and 90% fat content. In addition, independent of the diet, a significantly higher ketone buildup capability of subjects with higher resting energy expenditure (R[superscript 2] = 0.92), and lower body mass index (R[superscript 2] = 0.71) was observed during FK.

We investigate near-field radiative heat transfer between Indium Tin Oxide (ITO) nanowire arrays which behave as type 1 and 2 hyperbolic metamaterials. Using spatial dispersion dependent effective medium theory to model the dielectric function of the nanowires, the impact of filling fraction on the heat transfer is analyzed. Depending on the filling fraction, it is possible to achieve both types of hyperbolic modes. At 150 nm vacuum gap, the heat transfer between the nanowires with 0.5 filling fraction can be 11 times higher than that between two bulk ITOs. For vacuum gaps less than 150 nm the heat transfer increases as the filling fraction decreases. Results obtained from this study will facilitate applications of ITO nanowires as hyperbolic metamaterials for energy systems.

Many drugs are effective in the early stage of treatment, but patients develop drug resistance after a certain period of treatment, causing failure of the therapy. An important example is Herceptin, a popular monoclonal antibody drug for breast cancer by specifically targeting human epidermal growth factor receptor 2 (Her2). Here we demonstrate a quantitative binding kinetics analysis of drug-target interactions to investigate the molecular scale origin of drug resistance. Using a surface plasmon resonance imaging, we measured the in situ Herceptin-Her2 binding kinetics in single intact cancer cells for the first time, and observed significantly weakened Herceptin-Her2 interactions in Herceptin-resistant cells, compared to those in Herceptin-sensitive cells. We further showed that the steric hindrance of Mucin-4, a membrane protein, was responsible for the altered drug-receptor binding. This effect of a third molecule on drug-receptor interactions cannot be studied using traditional purified protein methods, demonstrating the importance of the present intact cell-based binding kinetics analysis.

Research has shown that construction projects in Saudi Arabia have exhibited poor performance for the past three decades. The traditional risk management practices have been ineffective at helping contractors deliver projects on time and within budget while meeting quality expectations. Studies have identified that client decision making is one of the main causes of risks that occur on projects in Saudi Arabia. This paper proposes a new risk management model that can minimize client decision making, and enable the client to utilize expertise, thereby improving project quality and performance. The model is derived from the Information Measurement Theory (IMT) and Performance Information Procurement System (PIPS), both developed at Arizona State University in the United States (U.S.). The model has been tested over 1800 times in both construction and non-construction projects, showing a decrease in required management by owner by up to 80% and an increase in efficiency up to 40%.

Brazil has had issues in efficiently providing the required amount of electricity to its citizens at a low cost. One of the main causes to the decreasing performance of energy is due to reoccurring droughts that decrease the power generated by hydroelectric facilities. To compensate for the decrease, Brazil brought into use thermal power plants. The power plants being on average 23.7% more expensive than hydroelectric. Wind energy is potentially an alternative source of energy to compensate for the energy decrease during droughts. Brazil has invested in wind farms recently, but, due to issues with the delivery method, only 34% of wind farms are operational. This paper reviews the potential benefit Brazil could receive from investing more resources into developing and operating wind farms. It also proposes that utilization of the best value approach in delivering wind farms could produce operational wind farms quicker and more efficiently than previously experienced.

Delays are a major cause for concern in the construction industry in Saudi Arabia. This paper identifies the main causes of delay in infrastructure projects in Mecca, Saudi Arabia, and compares these with projects around the country and other Gulf countries. Data was obtained from 49 infrastructure projects undertaken by the owner and were analyzed quantitatively to understand the severity and causes of delay. 10 risk factors were identified and were grouped into four categories. Average delay in infrastructure projects in Mecca was found to be 39%. The most severe cause of delay was found to be the land acquisition factor. This highlights the critical land ownership and acquisition issues that are prevailing in the city. Additionally, other factors that contribute to delay include contractors’ lack of expertise, re-designing, and haphazard underground utilities (line services). It is concluded that the majority of project delays were caused from the owner's side as compared to contractors, consultants, and other project's stakeholders. This finding matched with the research findings of the Gulf Countries Construction (GCC) Industry's literature. This study fills an important practice and research gap for improving the efficiency in delivering infrastructure projects in the holy city of Mecca and Gulf countries at large.

This paper is part of doctoral research to improve the current Saudi Arabian (SA) procurement system. SA has the largest construction market in the Middle East. However, the use of the traditional procurement system in SA has been identified as one of the causes for poor performance in the delivery of construction. The system has been identified as a major risk to the SA government, due to consistent increased costs and delays of up to 70% on projects. A survey was conducted with 1396 participants including engineers, buyers, contractors, consultants, academics, and architects. The purpose of the survey was to identify the validity of the recent claims that the procurement system in SA is broken. The participants work in both the private and government sectors. The survey results showed that the procurement system is a major risk to projects, affects construction projects negatively, and is in need of improvement.

In this letter, we study the near-field radiative heat transfer between two metamaterial substrates coated with silicon carbide (SiC) thin films. It is known that metamaterials can enhance the near-field heat transfer over ordinary materials due to excitation of magnetic plasmons associated with s polarization, while strong surface phonon polariton exists for SiC. By careful tuning of the optical properties of metamaterial, it is possible to excite electrical and magnetic resonances for the metamaterial and surface phonon polaritons for SiC at different spectral regions, resulting in the enhanced heat transfer. The effect of the SiC film thickness at different vacuum gaps is investigated. Results obtained from this study will be beneficial for application of thin film coatings for energy harvesting.

A film-coupled metamaterial structure is numerically investigated for enhancing the light absorption in an ultrathin photovoltaic layer of crystalline gallium arsenide (GaAs). The top subwavelength concave grating and the bottom metallic film could not only effectively trap light with the help of wave interference and magnetic resonance effects excited above the bandgap, but also practically serve as electrical contacts for photon-generated charge collection. The energy absorbed by the active layer is greatly enhanced with the help of the film-coupled metamaterial structure, resulting in significant improvement on the short-circuit current density by three times over a free-standing GaAs layer at the same thickness. The performance of the proposed light trapping structure is demonstrated to be little affected by the grating ridge width considering the geometric tolerance during fabrication. The optical absorption at oblique incidences also shows direction-insensitive behavior, which is highly desired for efficiently converting off-normal sunlight to electricity. The results would facilitate the development of next-generation ultrathin solar cells with lower cost and higher efficiency.

Exposure to fine particles can cause various diseases, and an easily accessible method to monitor the particles can help raise public awareness and reduce harmful exposures. Here we report a method to estimate PM air pollution based on analysis of a large number of outdoor images available for Beijing, Shanghai (China) and Phoenix (US). Six image features were extracted from the images, which were used, together with other relevant data, such as the position of the sun, date, time, geographic information and weather conditions, to predict PM_2.5 index. The results demonstrate that the image analysis method provides good prediction of PM_2.5 indexes, and different features have different significance levels in the prediction.