The emergence of biofilms:Computational and experimental studies

The response of biofilms to any external stimuli is a cumulative response aggregated from individual bacteria residing within the biofilm. The organizational complexity of biofilm can be studied effectively by understanding bacterial interactions at cell level. The overall aim of the thesis is to explore the complex evolutionary behaviour of bacterial biofilms. This thesis is divided into three major studies based on the type of perturbation analysed in the study. The first study analyses the physics behind the development of mushroom-shaped structures from the influence of nutrient cues in biofilms. Glazier-Graner-Hogeweg model is used to simulate the cell characteristics. From the study, it is observed that chemotaxis of bacterial cells towards nutrient source is one of the major precursors for formation of mushroom-shaped structures. The objective of the second study is to analyse the impact of environmental conditions on the inter-biofilm quorum sensing (QS) signalling. Using a hybrid convection-diffusion-reaction model, the simulations predict the diffusivity of QS molecules, the spatiotemporal variations of QS signal concentrations and the competition outcome between QS and quorum quenching mutant bacterial communities. The mechanical effects associated with the fluid-biofilm interaction is addressed in the third study. A novel fluid-structure interaction model based on fluid dynamics and structural energy minimization is developed in the study. Model simulations are used to analyse the detachment and surface effects of the fluid stresses on the biofilm. In addition to the mechanistic models described, a separate study is carried out to estimate the computational efficiency of the biofilm simulation models

Housekeeping inspection and inventory analysis are the primary responses of engineering and logistics operations in hospitality industry- An intensive case study of professional research on Sheraton Gateway Hotel in Toronto Pearson International Airport,

Housekeeping inspection maintains a chronological checklist and it has the major practice for the hospitality industry. Hospitality industry describes an imaging services to restaurants, lodging, event planning, theme parks, transportation, cruise line, tourism industry etc. Now days, hospitality as well as housekeeping are a dynamic and vibrant industry. It has different facilities of maintenances and direct operations with technology, engineering, housekeeping, kitchen workers, marketing, human resources, bartenders and supply chain logistic management

Sarcasm Detection: A Comparative Analysis of RoBERTa-CNN vs RoBERTa-RNN Architectures

Increasingly advanced technology and the creation of social media and the internet can become a forum for people to express things or opinions. However, comments or views from users sometimes contain sarcasm making it more difficult to understand. News headlines, sometimes contain sarcasm which makes readers confused about the content of the news. Therefore, in this research, a model was created for sarcasm detection. Many methods are used for sarcasm detection, but performance still needs to be improved. So this research aims to compare the performance of two text classification methods, Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN), in detecting sarcasm in English news headlines using RoBERTa text transformation.  RoBERTa produces a fixed-size vector of numbers 1x768. The research results show that CNN has better performance than RNN. CNN achieved the highest average accuracy of 0.891, precision of 0.878, recall of 0.874, and f1-score of 0.876, with a loss of 0.260 and a processing time of 508.1 milliseconds per epoch. On the contrary, RNN shows an accuracy of 0.711, precision of 0.692, recall of 0.620, f1-score 0.654, and loss of 0.564, with a longer processing time of 116500 milliseconds per epoch. The 10-fold cross-validation evaluation method ensures the model performs well and avoids overfitting. So it is recommended to use the combination of RoBERTa and CNN in other text classification applications that require high speed and accuracy. Further research is recommended to explore deeper CNN architectures or other architectural variations such as Transformer-based models for performance improvements