Developing New Methodologies For Electro-Organic Chemistry

Electro-Organic Chemistry has great potential to be used extensively in chemical synthesis but remains relatively under-exploited. To help expand this promising field of research, this PhD project was centred around developing new electrochemical methodology for use in organic reactions, particularly through activation of iodide ions and organic iodide compounds. The first research part is related to iodide ions: a dual electrochemical oxidative process in synthesizing the dihydrobenzofuran motif with excellent efficiency has been developed. This allowed for the formation of zinc ions and molecular iodine from a carbon anode in one pot followed by a zinc catalysed iodocyclisation of phenol and alkene. Related mechanistic studies indicate an unusual pathway which explains the selectivity of the reaction without the often-problematic electrophilic aromatic iodination of the aryl ring. Further extension of this newly developed method to aniline has led to a novel aziridine intermediate which ultimately yielded a diaminated product. The second research part is related to the activation of organic iodide compounds. A newly developed electrochemical approach allows the generation of C-centered radicals for radical addition reaction and cyclisation. The yields and efficiency of the processes are superior in most cases to comparable conditions with tributyltin hydride. The use of air and electricity as the promotor combined with the aqueous reaction media make this a clean and ‘green’ alternative to these classic C–C bond forming processes. We have described the reaction mechanism in terms of electrogenerated reactive oxygen species which arises from the reduction of molecular oxygen in the aqueous phase. In addition, for easy and cheap access to this area and enable reactions that require for anhydrous conditions, we have successfully applied a supermarket purchased battery and UV light respectively as replacements of the roles of potentiostat equipment and oxygen in the methodology

Profile Interview with Dr. Jeralyn Faris

Faris’s dissertation was a 4-year qualitative study of the Tippecanoe County Problem Solving Reentry Court. Dr. Faris explains: “I took a criminal justice course taught by Dr. JoAnn Miller, who was committed to using her knowledge to better the community. She designed the Reentry Court and invited me to serve with her on the team that supported ex-prisoners, men and women, returning to the community after years of incarceration. The team met with and advised the judge, attending weekly court sessions with ex-prisoners. The court provided support and accountability, and I participated for over four years, assisting a total of 98 men and women in their navigation of reentry challenges—housing, employment, addiction temptations, family reunification, and a myriad of other issues. It was an amazing experience.” Dr. Faris became entrenched in her commitment to community engagement and service-learning models because she experienced the impact of learning as she supported and advocated for the ex-prisoners in the court system

Neural Video Recovery for Cloud Gaming

Cloud gaming is a multi-billion dollar industry. A client in cloud gaming sends its movement to the game server on the Internet, which renders and transmits the resulting video back. In order to provide a good gaming experience, a latency below 80 ms is required. This means that video rendering, encoding, transmission, decoding, and display have to finish within that time frame, which is especially challenging to achieve due to server overload, network congestion, and losses. In this paper, we propose a new method for recovering lost or corrupted video frames in cloud gaming. Unlike traditional video frame recovery, our approach uses game states to significantly enhance recovery accuracy and utilizes partially decoded frames to recover lost portions. We develop a holistic system that consists of (i) efficiently extracting game states, (ii) modifying H.264 video decoder to generate a mask to indicate which portions of video frames need recovery, and (iii) designing a novel neural network to recover either complete or partial video frames. Our approach is extensively evaluated using iPhone 12 and laptop implementations, and we demonstrate the utility of game states in the game video recovery and the effectiveness of our overall design

Beyond Reward: Offline Preference-guided Policy Optimization

This study focuses on the topic of offline preference-based reinforcement learning (PbRL), a variant of conventional reinforcement learning that dispenses with the need for online interaction or specification of reward functions. Instead, the agent is provided with fixed offline trajectories and human preferences between pairs of trajectories to extract the dynamics and task information, respectively. Since the dynamics and task information are orthogonal, a naive approach would involve using preference-based reward learning followed by an off-the-shelf offline RL algorithm. However, this requires the separate learning of a scalar reward function, which is assumed to be an information bottleneck of the learning process. To address this issue, we propose the offline preference-guided policy optimization (OPPO) paradigm, which models offline trajectories and preferences in a one-step process, eliminating the need for separately learning a reward function. OPPO achieves this by introducing an offline hindsight information matching objective for optimizing a contextual policy and a preference modeling objective for finding the optimal context. OPPO further integrates a well-performing decision policy by optimizing the two objectives iteratively. Our empirical results demonstrate that OPPO effectively models offline preferences and outperforms prior competing baselines, including offline RL algorithms performed over either true or pseudo reward function specifications. Our code is available on the project website: https://sites.google.com/view/oppo-icml-2023

Numerical analysis of hard rock tunnel excavated by double shield TBM based on CWFS model

In order to study the mechanical response of a hard rock tunnel excavated by double shield tunnel boring machine (DS-TBM), a numerical method was introduced to simulate the TBM excavating process. The failure modes of surrounding rock mass described based on the cohesion weakening and frictional strengthening (CWFS) Mohr-Coulomb strain-softening criterion. The characteristics of stress field and plastic zone on the cross and longitudinal section of the tunnel were analyzed in detail, and the results were compared with those in the intrinsic condition (when TBM model is not activated). The simulation results indicate that the stress paths at the vault are relatively simple, and the stress concentration caused by excavation unloading is obviously reduced by lining and backfill grouting, while the sidewall is less disturbed by the excavation of TBM. The invert experiences three unloading processes, due to excavation, the contact between the rear shield and the bottom of surrounding rock, as well as backfill grouting at gap between the lining and the rock mass. The vault has a larger plastic zone than the invert, attributing to the geometrical difference between the cutter-head and the front shield, as well as the conicity of the front and rear shields. The area of plastic zones gradually increases along the tunnel route, but it becomes stable after the rear shield

Energy efficiency and environmental degradation nexus: evidence from the Quantile-on-Quantile regression technique

The world is facing enormous challenge of climate change and global warming due to increased emission level. In order to overcome such challenges, economies are adopting energy efficient techniques to control the carbon emissions and improves environmental sustainability. This study analyses the influencing factors of environmental quality from a global perspective throughout the last three decades. In this regard, advanced time series approaches are used to identify the association between factors such as economic growth, energy efficiency (E.N.E.F.), and carbon emissions – covering global data over the period 1990Q4–2020Q4. From the time series methods, this study observed the stationarity of all variables at first difference. The empirical outcomes also validates the long-run equilibrium relationship between the variables. Due to asymmetric distribution of the variables, this study uses the novel Quantile-on-Quantile (Q.Q.) regression approach, which reveals that increasing economic growth harms environmental quality by increasing the carbon emissions level. However, E.N.E.F. is a prominent factor of environmental sustainability, that reduces the level of carbon emissions in the atmosphere. Employing the pairwise Granger causality test, this study observed the unidirectional causality from economic growth to carbon emissions, while a two-way causal nexus is found between economic growth – E.N.E.F. and E.N.E.F. – carbon emissions. Based on the empirical results, this study suggests that economic growth should be regulated in a sense that it contribute towards the improvement of E.N.E.F., which ultimately leads to reduce the emissions level and promote environmental sustainability