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

Temporal-spatial analysis of a foot-and-mouth disease model with spatial diffusion and vaccination

Foot-and-mouth disease is an acute, highly infectious, and economically significant transboundary animal disease. Vaccination is an efficient and cost-effective measure to prevent the transmission of this disease. The primary way that foot-and-mouth disease spreads is through direct contact with infected animals, although it can also spread through contact with contaminated environments. This paper uses a diffuse foot-and-mouth disease model to account for the efficacy of vaccination in managing the disease. First, we transform an age-space structured foot-and-mouth disease into a diffusive epidemic model with nonlocal infection coupling the latent period and the latent diffusive rate. The basic reproduction number, which determines the outbreak of the disease, is then explicitly formulated. Finally, numerical simulations demonstrate that increasing vaccine efficacy has a remarkable effect than increasing vaccine coverage

She? The Role of Perceived Agent Gender in Social Media Customer Service

This work investigated the role of perceived agent gender in customer behavior using a unique dataset from Southwest Airlines’ Twitter account. We inferred agent gender based on the first names provided by agents when responding to customers. We measured customer behavior using three outcomes: whether a customer decided to continue the service conversation upon receiving an agent’s initial response as well as the valence and arousal levels in their second tweet if the customer chose to continue the interaction. Our identification strategy relied on the Backdoor Criterion and hinged on the assumption that customer service requests are assigned to the next available agent, independent of agent gender. The findings revealed that customers were more likely to continue interactions with female agents than male agents and they were more negative in valence but less intense in arousal with the former group than with the latter

She? The Role of Perceived Agent Gender in Social Media Customer Service

This paper investigated the role of perceived agent gender in customer behavior using a unique dataset from Southwest Airlines\u27 Twitter account. We inferred agent gender based on the first names provided by agents when responding to customers. We measured customer behavior using three outcomes: whether a customer decided to continue the service conversation upon receiving an agent’s initial response as well as the valence and arousal levels in their second tweet if the customer chose to continue the interaction. Our identification strategy relied on the Backdoor Criterion and hinged on the assumption that customer service requests are assigned to the next available agent, independent of agent gender. The findings revealed that customers were more likely to continue interactions with female agents than male agents and they were more negative in valence but less intense in arousal with the former group than with the latter

A Differential Equation Model of HIV Infection of CD

An epidemic model of HIV infection of CD4+ T-cells with cure rate and delay is studied. We include a baseline ODE version of the model, and a differential-delay model with a discrete time delay. The ODE model shows that the dynamics is completely determined by the basic reproduction number R01, a unique endemic equilibrium exists and is globally stable in the interior of the feasible region. In the DDE model, the delay stands for the incubation time. We prove the effect of that delay on the stability of the equilibria. We show that the introduction of a time delay in the virus-to-healthy cells transmission term can destabilize the system, and periodic solutions can arise through Hopf bifurcation

X-PuDu at SemEval-2022 Task 7: A Replaced Token Detection Task Pre-trained Model with Pattern-aware Ensembling for Identifying Plausible Clarifications

This paper describes our winning system on SemEval 2022 Task 7: Identifying Plausible Clarifications of Implicit and Underspecified Phrases in Instructional Texts. A replaced token detection pre-trained model is utilized with minorly different task-specific heads for SubTask-A: Multi-class Classification and SubTask-B: Ranking. Incorporating a pattern-aware ensemble method, our system achieves a 68.90% accuracy score and 0.8070 spearman's rank correlation score surpassing the 2nd place with a large margin by 2.7 and 2.2 percent points for SubTask-A and SubTask-B, respectively. Our approach is simple and easy to implement, and we conducted ablation studies and qualitative and quantitative analyses for the working strategies used in our system.Comment: Accepted at the 16th International Workshop on Semantic Evaluation (SemEval-2022), NAAC