Manufacturing of nanocrystalline cellulose

Nanocrystalline cellulose (CNC) has attracted considerable attention over the last several decades in many fields. Sulfuric acid hydrolysis is utilized as the state-of-the-art for producing nanocrystalline cellulose nowadays. The common conditions of H2SO4 standard method used 1 735% of acid dosage, 64% of acid concentration, 45oC of temperature and 45 minutes of reaction time. The purpose of this thesis is to develop and determine a novel modified manufacturing method for CNC production process by adjusting the reaction circumstances. More precisely, this study investigates the possibility for using lower acid amount and higher temperature (65oC-85oC) to produce nanocrystalline cellulose. The raw material in the production procedures was microcrystalline cellulose (MCC). CNCs were extracted from MCC by sulfuric acid. Various reaction conditions (acid dosage, temperature, acid concentration, reaction time) were changed based on the performance of CNC yield and quality in order to obtain the optimal circumstances. The morphology and dimensions of CNCs were investigated by scanning electron microscopy (SEM) and transmission electron microscopy (TEM). The chemical structure and crystallinity were measured by Fourier transform infrared spectroscopy (FTIR) and X-ray diffraction (XRD). Thermogravimetric analysis (TGA) was utilized to study the thermal stability of CNCs. The results from the characterization methods demonstrated that using lower sulfuric acid dosage and higher temperature could also produce CNCs with promising yield and qualities as standard method. The optimal hydrolysis conditions for modified method are described as follow: 700% of acid dosage, 65oC of temperature, 63% of acid concentration and 20 minutes of reaction time. Based on the modified method, CNCs could have a maximum yield of 30.6% of and crystallinity of 79.3%. The average length of CNCs could be 183.1nm and the mean diameter was 7.6nm. The preliminary economy analysis illustrated that applying modified method provided better economy than the existing standard method

A hegemonic form of transfer pricing: The case of a Chinese organisation

Recent decades have witnessed an understanding of management accounting as a social practice with alternative social theory explanations about its practice varieties but transfer pricing issues therein have inadvertently been neglected. Based on 7-month fieldwork in a Chinese privately-owned group enterprise, this thesis attempts at understanding transfer pricing practices differently vis-a-vis power relations in that organisation. I have focused on interrelationships between dominant and dominated social groups in this organisation to examine how transfer pricing has become a political phenomenon. The theoretical framework based on the Gramscian hegemony captures these interrelationships. The findings illustrate that the political development in China conditioned the hegemonic relationship in Chinese organisations while the ideology of Confucianism determined certain control practice. As business owners are most powerful, mundane controls are rather informal while formal controls are largely de-coupled from operation. Consequently, transfer pricing practice is determined by hegemonic interests and ideologies of the dominant leading to a situation where managers, who perform transfer pricing calculation, rely on a “common sense”. However, agreement is still possible as Confucianism provided good sense of harmony and concern-of-others. This study contributes to transfer pricing literature by highlighting the social complexity of transfer pricing, while contributes to hegemony theory by showing the dynamic nature of hegemony

A Contagion Model of Emergency Airplane Evacuations

Motivated by the Asiana Flight 214 crash in San Francisco this summer, this project focuses on modeling an emergency airplane evacuation. Our models are based on the Particle Swarm Optimization (PSO) algorithm, where each agent\u27s position is compared to a fitness function that describes the current environment. Each agent moves according to its knowledge of its own previous best position and the group\u27s current best position. The static environment is modeled by a potential function that describes the layout of the airplane that includes the exits and physical barriers such as the seats. We model the interactions within the swarm by an attraction-repulsion force. Finally, we chose to incorporate the spread of an emotion such as fear or panic that influences the behavior of agents within the swarm. Our project includes an analysis of how the parameters and scaling of different parts of the model affect the swarm behavior. We also compared simulations with and without fear to study the impact of emotion on individual behavior as well as the ability of the entire group to safely exit the aircraft. We hope that this will lead to increased understanding of how panicked crowds behave in evacuation situations and that this will lead to better, safer evacuation designs

Topics in small area estimation with applications to the National Resources Inventory

A practical application of small area estimation in the National Resources Inventory, a large survey of the non-federal land area in the United States, is described. Several estimation issues raised by this application are discussed as motivation for theoretical investigation of some aspects of small area estimation;The situation in which individual small area sampling variances are directly estimated is studied. This situation is not covered by standard asymptotic results (Prasad and Rao (1990)), which assume that a finite-dimensional parameter characterizes the small area variances. An approximation for the mean square error (MSE) of the empirical best linear unbiased predictor and an estimator of the MSE are developed. Simulation studies show that the theoretical expressions are good approximations for the MSE of the predictors. Also the suggested MSE estimator has smaller overestimation for the MSE than related estimators in the literature when the between-area variance component is small;Small area estimation under a restriction, which forces small area estimates to sum to the direct estimate for a large area, is discussed. A criterion that unifies the derivation of several restricted estimators is proposed. The estimator that is the unique best linear unbiased estimator under the criterion is derived and an approximation for the MSE of the restricted estimator is presented;The bias of the empirical best linear unbiased predictor is assessed for the model in which the sampling errors are not normally distributed. The robustness of the MSE estimator is examined under non-normal error distributions by using simulations. The simulations also demonstrate that imposing a restriction can reduce the bias when the errors are not symmetrically distributed

Downlink Rate Analysis for Virtual-Cell Based Large-Scale Distributed Antenna Systems

Despite substantial rate gains achieved by coordinated transmission from a massive amount of geographically distributed antennas, the resulting computational cost and channel measurement overhead could be unaffordable for a large-scale distributed antenna system (DAS). A scalable signal processing framework is therefore highly desirable, which, as recently demonstrated in \cite{Dai_TWireless}, could be established based on the concept of virtual cell. In a virtual-cell based DAS, each user chooses a few closest base-station (BS) antennas to form its virtual cell, that is, its own serving BS antenna set. In this paper, we focus on a downlink DAS with a large number of users and BS antennas uniformly distributed in a certain area, and aim to study the effect of the virtual cell size on the average user rate. Specifically, by assuming that maximum ratio transmission (MRT) is adopted in each user's virtual cell, the achievable ergodic rate of each user is derived as an explicit function of the large-scale fading coefficients from all the users to their virtual cells, and an upper-bound of the average user rate is established, based on which a rule of thumb is developed for determining the optimal virtual cell size to maximize the average user rate. The analysis is further extended to consider multiple users grouped together and jointly served by their virtual cells using zero-forcing beamforming (ZFBF). In contrast to the no-grouping case where a small virtual cell size is preferred, it is shown that by grouping users with overlapped virtual cells, the average user rate can be significantly improved by increasing the virtual cell size, though at the cost of a higher signal processing complexity

Det6D: A Ground-Aware Full-Pose 3D Object Detector for Improving Terrain Robustness

Accurate 3D object detection with LiDAR is critical for autonomous driving. Existing research is all based on the flat-world assumption. However, the actual road can be complex with steep sections, which breaks the premise. Current methods suffer from performance degradation in this case due to difficulty correctly detecting objects on sloped terrain. In this work, we propose Det6D, the first full-degree-of-freedom 3D object detector without spatial and postural limitations, to improve terrain robustness. We choose the point-based framework by founding their capability of detecting objects in the entire spatial range. To predict full-degree poses, including pitch and roll, we design a ground-aware orientation branch that leverages the local ground constraints. Given the difficulty of long-tail non-flat scene data collection and 6D pose annotation, we present Slope-Aug, a data augmentation method for synthesizing non-flat terrain from existing datasets recorded in flat scenes. Experiments on various datasets demonstrate the effectiveness and robustness of our method in different terrains. We further conducted an extended experiment to explore how the network predicts the two extra poses. The proposed modules are plug-and-play for existing point-based frameworks. The code is available at https://github.com/HITSZ-NRSL/De6D.Comment: 8 pages, 9 figures, submit to RA-