ATOM ECONOMICAL REACTIONS OF TERPENOIDS AND POST-CONSUMER PLASTICS WITH SULFUR

The production of Ordinary Portland Cement (OPC) and its uses have a complicated environmental impact, which is influenced by infrastructure development and building operations as well as CO2 emissions, which account for 7% of all worldwide CO2 emissions. Due to the increasing population, production is still ongoing. The search for cement and construction materials produced with zero to low CO2 emissions is therefore continuous. Finding recyclable, CO2 gas-free biocomposites with high sulfur content that can rival the mechanical properties of popular building supplies like Portland cement is the primary objective of the research discussed in this dissertation. Chapter one describes the terpenoid as a substitution for olefins in commercially derived polymers. There is an emerging trend in substituting petrochemical derived polymers with sustainable biopolymers to the most commonly used commercial polymers. Terpenoid is an abundant, inexpensive monomer which as characteristic properties and most importantly, polymerizable alkene. Therefore, terpenoids have undergone relatively significant research as starting material for polymer synthesis. Chapter two elaborates the process of inverse vulcanization used to create composites of sulfur-crosslinked terpenoids. It is investigated how High Sulfur Materials (HSMs) with a biopolymeric agent can create a stable sulfur network. Also included are the mechanical, thermal, and mechanical integrity aspects of recycling. And it discusses the investigation of terpenoid cyclization mechanism. The degree to which the amount of terpenoid olefins and the ratio of sulfur to terpenoid affect the thermal and mechanical properties of such terpenoid-sulfur composites is described in the chapter three. Plastic waste pollution of the environment is becoming a significant concern to both human health and the ecosystem. However, even post-consumer plastics that could be easily recycled using straightforward chemical procedures are nonetheless frequently dumped in landfills. Chapter four presents a straightforward, one-stage method for chemically recycling PET to produce composites with mechanical and thermal properties that are competitive with those of widely used structural materials like Portland cement. Chapter five reports the creation and evaluation of mechanical, morphological and thermal properties of a group of composites made of different proportion of guaiacol generated from lignin, fatty acids, and sulfur. To evaluate the impact of unsaturation level on composites, monounsaturated oleic acid and diunsaturated linoleic acid were both employed as the fatty acid components

Effective Record Linkage Techniques for Complex Population Data

Real-world data sets are generally of limited value when analysed on their own, whereas the true potential of data can be exploited only when two or more data sets are linked to analyse patterns across records. A classic example is the need for merging medical records with travel data for effective surveillance and management of pandemics such as COVID-19 by tracing points of contacts of infected individuals. Therefore, Record Linkage (RL), which is the process of identifying records that refer to the same entity, is an area of data science that is of paramount importance in the quest for making informed decisions based on the plethora of information available in the modern world. Two of the primary concerns of RL are obtaining linkage results of high quality, and maximising efficiency. Furthermore, the lack of ground-truth data in the form of known matches and non-matches, and the privacy concerns involved in linking sensitive data have hindered the application of RL in real-world projects. In traditional RL, methods such as blocking and indexing are generally applied to improve efficiency by reducing the number of record pairs that need to be compared. Once the record pairs retained from blocking are compared, certain classification methods are employed to separate matches from non-matches. Thus, the general RL process comprises of blocking, comparison, classification, and finally evaluation to assess how well a linkage program has performed. In this thesis we initially provide a holistic understanding of the background of RL, and then conduct an extensive literature review of the state-of-the-art techniques applied in RL to identify current research gaps. Next, we present our initial contribution of incorporating data characteristics, such as temporal and geographic information with unsupervised clustering, which achieves significant improvements in precision (more than 16%), at the cost of minor reduction in recall (less than 2.5%) when they are applied on real-world data sets compared to using regular unsupervised clustering. We then present a novel active learning-based method to filter record pairs subsequent to the record pair comparison step to improve the efficiency of the RL process. Furthermore, we develop a novel active learning-based classification technique for RL which allows to obtain high quality linkage results with limited ground-truth data. Even though semi-supervised learning techniques such as active learning methods have already been proposed in the context of RL, this is a relatively novel paradigm which is worthy of further exploration. We experimentally show more than 35% improvement in clustering efficiency with the application of our proposed filtering approach; and linkage quality on par with or exceeding existing active learning-based classification methods, compared to our active learning-based classification technique. Existing RL evaluation measures such as precision and recall evaluate the classification outcome of record pairs, which can cause ambiguity when applied in the group RL context. We therefore propose a more robust RL evaluation measure which evaluates linkage quality based on how individual records have been assigned to clusters rather than considering record pairs. Next, we propose a novel graph anonymisation technique that extends the literature by introducing methods of anonymising data to be linked in a human interpretable manner, without compromising structure and interpretability of the data as with existing state-of-the-art anonymisation approaches. We experimentally show how the similarity distributions are maintained in anonymised and original sensitive data sets when our anonymisation technique is applied, which attests to its ability to maintain the structure of the original data. We finally conduct an empirical evaluation of our proposed techniques and show how they outperform existing RL methods

ANALISIS WACANA KRITIS ILUSTRASI EDUKASI KOMIK COVID-19 WATIEK IDEO

Komik ilustrasi edukasi covid merupakan sebuah komik anak yang menarik untuk diteliti karena mengangkat realitas kehidupan tentang kebijakan-kebijakan yang terjadi pada saat pandemi covid. Penelitian ini bertujuan untuk melihat bagaimana teks, kognisi sosial serta konteks sosial yang terdapat pada komik ilustrasi edukasi covid. Pada penelitian ini, peneliti menggunakan pendekatan kualitatif dengan analisis wacana yang dikembangkan oleh Teun A. Van Dijk. Analisis wacana yang dikembangkan oleh Van dijk memiliki tiga dimensi, yaitu dimensi teks, kognisi sosial dan konteks sosial. Pada dimensi teks yang akan dilihat ialah struktur mikro, struktur makro dan supertruktur. Kognisi sosial ialah pengetahuan serta kesadaran mental pembuat teks dalam memproduksi teks. Selanjutnya, konteks sosial ialah pengetahuan yang terkait dengan situasi yang berkembang di masyarakat. Melalui strategi analisis wacana Teun A. Van Dijk, peneliti menemukan bahwa struktur teks pada komik ilustrasi covid menggunakan kalimat aktif. Informasi-informasi disampaikan dengan cara sederhana mengingat komik tersebut awalnya ditujukan untuk anak-anak sebelum dipakai untuk edukasi pada masyarakat luas. Koherensi antar kalimat yang saling terkait dan memberikan kesaatuan makna yang utuh. Strategi wacana model Van dijk juga mengungkapkan bahwa komik edukasi covid merupakan representasi dari keadaan pada saat pandemi, yaitu tentang kebijakan-kebijakan pada saat pandemi covid. Kata kunci: Komik, Analisis wacana kritis, covi

Efficient population record linkage with temporal and spatial constraints.

Objectives Population databases containing birth, death, and marriage certificates or census records, are increasingly used for studies in a variety of research domains. Their large scale and complexity make linking such databases highly challenging. We present a scalable blocking and linking technique that exploits temporal and spatial constraints in personal data. Approach Based on a state-of-the-art blocking method using locality sensitive hashing (LSH), we incorporate (a) attribute similarities, (b) temporal constraints (for example, a mother cannot give birth to two babies less than nine months apart, besides a multiple birth), and (c) spatial constraints (two births by the same mother are more likely to happen in the same location than far apart). In an iterative fashion, we identify highly confident matches first, and use these matches to further refine our constraints. We adopt a block size and frequency-based filtering approach to further enhance the efficiency of the record linkage comparison step. Results We conducted experiments on a Scottish data set containing 17,613 birth certificates from 1861 to 1901, where the application of standard LSH blocking generated approximately 15 million candidate record pairs, with a recall of 0.999 and a precision of 0.003. With the application of our block size and frequency-based filtering approach we obtained a ten-fold and hundred-fold reduction of this candidate record pair set with a small reduction of recall to 0.984 and 0.962, respectively. The comparison of record pairs in the hundred-fold reduction using our iterative linking technique achieved up-to 0.961 precision and 0.811 recall. This means that our method can achieve a reduction in computational efforts, and improvement in precision of over 99% at the cost of a decline in recall below 19%. Conclusion We presented a method to reduce the computational complexity of linking large and complex population databases while ensuring high linkage quality. Our method can be generalised to population databases where temporal and spatial constraints can be defined. We plan to apply our method on a Scottish database with 24 million records