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The development of third-generation photovoltaic (TGPV) technologies promises to address some of the limitations of conventional silicon-based solar cells. However, the scalability of these technologies is a critical factor in their practical application. TGPV include Perovskite Solar Cells (PSC), Organic Solar Cells (OSC), and Dye-Sensitized Solar Cell (DSSC). This paper reviews and summarizes the recent trends and research on the deposition techniques and modularity of TGPV. Various deposition techniques such as slot-die coating, thermal evaporation, and spray pyrolysis are discussed, along with their advantages and limitations. Modularity, which allows for the integration of TGPV cells into different structures, is also examined as a critical factor in scalability. The paper concludes that the scalability of TGPV technologies depends on the development of efficient and cost-effective deposition techniques and modularity, which will facilitate the integration of the TGPV cells into various structures and enable the widespread use of these promising technologies.

Many Internet of Things (IoT) applications deploy identical end devices like sensor nodes or surveillance cameras in an organization. The purpose of this thesis was to determine if a malicious physical substitution of one end device by an identical compromised device could be recognized using deep learning or machine learning techniques. Conventional techniques to address the physical replacement of a node require one to implement specialized hardware like Physical Unclonable Functions (PUF) using expensive encryption techniques. For low-resource devices like an ESP32, the device does not come with an integrated PUF module, and a separate chip is required to execute the cryptographic operations. Moreover, recently deep learning techniques are shown to have compromised the PUF-based technique as well. This thesis explored whether machine and deep learning could be used to identify a single end device from a set of identical devices without requiring a PUF-like mechanism for authentication. Network data from 18 identical ESP32 devices was collected in a typical MQTT-based IoT network. In addition to exploring conventional machine learning methods including Random Forest, Bayesian, SVM, LightGBM, Gradient Boosting, and XG-Boost, a tiny Convolutional Neural Network (CNN) was designed and optimized using the Hyperband algorithm. The CNN was small with 85,058 trainable parameters and only used the packet Inter-Arrival Time (IAT) as input. The results are that the CNN outperformed all other models, with a micro-F1-Score of 0.999 (0.0012). The Random Forest model was the best traditional machine learning models with a micro-F1-Score of 0.9501 (0.0036). The worst was Bayesian with a micro-F1-Score of 0.222 (0.0016). There was a statistically significant difference in the F1-Score (Kruskal-Wallis; chi-square=84.192, p < 0.05) between the various trained model using 10-fold validation.

The present study showcases the impact of digital transformation in the startup companies in the United Arab Emirates and the success factors. The study has formulated aims and objectives that identify the goal of the study. Besides, the research questions have also been developed in the introduction chapter. Here, the rationale and problem statement have also been described. An outline of research methodology has also been constructed here. This study analyzes the progress of the literature on digital startups and policy responses, evaluates its present focus and constraints critically, and offers an innovative viewpoint for further study. It examines the lifespan of digital companies by doing a thorough literature study, highlighting significant obstacles through the start until maturity. The study also underlines the need for flexible policy structures at the various levels to help business owners overcome obstacles related to financing, regulations, and integrating into the market. The results show that in order to provide comprehensive assistance throughout the startup phases, the regulatory structure should encourage creativity, secure finance, minimize laws in the earliest phases, assist developing and growth in the market, and encourage international cooperation. The literature review has been developed and provided various outcomes of the study. A detailed theoretical underpinning helps to emphasize the study properly. Besides, the strategies needed to be taken by the startups have also been described in the present chapter.