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The term packing refers to the arrangement of multiple geometrical structures or shapes such as circles, squares, triangles, or polygons into a fixed and finite set of points. The geometric structures to be packed can also be trees and paths. Packing is also possible in a 3-dimensional space with geometric structures such as spheres, cylinders, and cubes. The concept of packing was introduced more than half a century ago. Since then, many researchers have studied the packing strategies of different geometric structures in different configurations of point-set. Packing strategies help to construct and arrange multiple geometric structures in a predetermined bounded space; hence, it can be classified as an optimization problem, as we are trying to allocate the optimal space for resources in a finite bounded space. The better the efficiency of the algorithm, the greater number of items that can be packed. Packing geometrical structures have applications in the storage, transportation, and transmission of objects in fields like automobile, aerospace, and naval industries. Since, in real-life scenarios, resources are finite, and space is limited; thus it raises the question, how to efficiently use limited space for accommodating multiple resources. However, packing multiple geometric structures can raise some design considerations. In our research, we have studied the packing of non-self-crossing, edge-disjoint plane spanning paths and have obtained some promising results. We further address some design considerations and provide a different approach on packing at least two non-self-crossing, edge-disjoint plane spanning paths into a point-set.

The internet has transformed the way we shop. Previously, most of our purchases came in the form of shopping trips to a nearby store. Now, it’s as easy as clicking a mouse. But with great convenience comes great responsibility. We have to be constantly vigilant about our personal information. This work presents a novel approach for online buyers to have a trusted, decentralized, privacy-preserved physical assets delivery solution. The proposed solution focuses on privacy-preserving personal information in delivering physical assets between sellers and buyers. Our primary approach is to prevent sellers and agents (responsible for ensuring the asset delivery is carried out) from accessing the buyer's shipping information. Those data include personally identifiable information such as full name, phone number, email address, etc. Our framework encrypts the information printed on the physical packages (containing personal information) and stores that information into a decentralized database as encrypted data rather than keeping them in centralized databases using technologies like Blockchain, Ethereum, smart contracts and a commit-reveal scheme. In our framework, we used Blockchain, smart contracts to increase the transparency, accessibility, and integrity of the data and the commit-reveal scheme to preserve the confidentiality of the data. We present, implement and assess a blockchain-based system using Ethereum smart contracts. We present detailed algorithms that explain the details of our smart contract, security, cost and performance analysis of the proposed method. Our work indicates that the proposed solution is economically attainable and provides data integrity, security, transparency, and traceability.

The coronavirus 2019 pandemic introduced a new array of daily stressors, known as daily hassles, to the lives of university students. This study used the Stress, Appraisal, and Coping theory (Lazarus & Folkman, 1984) to understand how university students were affected by daily hassles and how they were coping with hassles-based stress, as well as how their companion animals provided social support during the stress and coping process. In this study, it was hypothesized that: (1) university students who experienced more daily hassles would experience higher perceived stress and lower overall life satisfaction; (2) university students who made effective use of coping strategies would experience lower perceived stress, and (3) university students who had higher perceived social support would experience lower perceived stress and higher overall life satisfaction. The study used a mixed-method approach that implemented a survey with measures of daily hassles, perceived stress, pandemic stress, coping strategies, social support, and overall life satisfaction. Open-ended questions were used to investigate the impact of stress and coping strategies, including the role of companion animals. Simple correlations and mediation analyses were used to analyze the measured responses, while a codebook thematic analysis was used to code the open-ended responses. The research findings showed that participants experienced hassles-based stress, and that effective coping strategies was associated with decreased stress and increased life satisfaction. However, social support from companion animals did not emerge as an effective coping strategy because it was not found to be associated with lower stress.

Inorganic phosphate (Pi) is an essential compound for all living things, as it serves as a component of nucleic acids and ATP, which are required for plant growth and proliferation. However, when excess Pi enters the aquatic environment, it has severe detrimental effects through the induction of harmful algal blooms (HAB), which negatively impact aquatic life, the quality of drinking water as well as recreational activities. It is therefore critical to have fast, sensitive, and inexpensive sensors for Pi that quickly and quantitively measure its concentration in a field setting. Phosphonates or organophosphorus compounds are phosphoric acids possessing alkyl or aryl linkages. Phosphonates (PN) are heavily used as antifouling agents in the oil industry. PN is added in high concentrations to the feedstock and act to block fouling. During this process, large amounts of PN are spread into the environment making the development of fast and accurate determination methods a requirement. Our study focused on working on two different peptides for detecting Pi and PN. Different numbers of amino acids were modeled through ChemDraw to finalize the best hexapeptides. We used the MM2 tool in ChemDraw to achieve minimized dansylated structure with and without Pi. The two peptides having a large distance between fluorophores are Ala Ala Arg Arg Gly Gly and Gly Gly Arg Arg Trp Gly. These hexapeptides were chosen to be titrated by different concentrations of Pi and PN from 4.88-5000 μM. Knowing the quenching properties of Pi and PN, we used the fluorescence signal as a sensor for them with an estimated detection limit of 4.88 μM.

Supervisory control and data acquisition (SCADA) systems are often imperiled bycyber-attacks, which can often be detected using intrusion detection system (IDSs).However, the performance and efficiency of IDSs can be affected by several factors,including the quality of data, curse of dimensionality of the data, and computationalcost. Feature reduction techniques can overcome most of these challenges by eliminatingthe redundant and non-informative features, thereby increasing the detectionaccuracy. This study aims to shows the importance of feature reduction on the intrusiondetection performance. To do this, a multi-modular IDS is designed that isconnected to the SCADA system of a water storage tank. A comparative study isalso performed by employing advanced feature selection and dimensionality reductiontechniques. The utilized feature reduction techniques improves the IDS efficiency byreducing the memory usage and using data with better quality, which in turn increasethe detection accuracy. The obtained results have been analyzed in terms of F1-scoreand accuracy.

This thesis was done in collaboration with Sorbonne University as part of a double degree Cotutelle. During development, cell differentiation frequently occurs upon signaling from concentration or activity gradients of molecules called morphogens. These molecules control in a dose-dependent manner the expression of sets of target genes that determine cell identity. A simple paradigm to study morphogens is the Bicoid gradient, which determines antero-posterior patterning in fruit fly embryos. The Bicoid transcription factor allows the rapid step-like expression of its major target gene hunchback, expressed only in the anterior half of the embryo. The general goal of my thesis was to understand how the information contained in the Bicoid morphogen gradient is rapidly interpreted to provide the precise expression pattern of its target. Using the MS2 system to fluorescently tag specific RNA in living embryos, we were able to show that the ongoing transcription process at the hunchback promoter is bursty and likely functions according to a two-state model. At each nuclear interphase, transcription is first observed in the anterior and it rapidly spreads towards the posterior, as expected for a Bicoid dose-dependent activation process. Surprisingly, it takes only 3 minutes from the first hints of transcription at the anterior to reach steady state with the setting of a sharp expression border in the middle of the embryo. Using modeling taking into account this very fast dynamics, we show that the presence of only 6 Bicoid binding sites (known number of sites in the hunchback promoter) in the promoter, is not sufficient to explain the establishment of a sharp expression border in such a short time. Thus, either more Bicoid binding sites or inputs from other transcription factors could help reconcile the model to the data. To better understand the role of transcription factors other than Bicoid in this process, I used a two-pronged strategy involving synthetic MS2 reporters combined with the analysis of the hunchback MS2 reporter in various mutant backgrounds. I show that the pioneer factor Zelda and the Hunchback protein itself are also critical for hunchback expression, maternal Hunchback acting at nuclear cycle 11-12, while zygotic Hunchback is acting later at nuclear cycle 13-14. The synthetic reporter approach indicate that in contrast to Hunchback and Caudal, Bicoid is able to activate transcription on its own when bound to the promoter. However, the presence of 6 Bicoid binding sites only leads to stochastic activation of the target loci. Interestingly, the binding of Hunchback to the Bicoid-dependent promoter reduces this stochasticity while Caudal might act as a posterior repressor gradient. Confronting these experimental data to theoretical models is ongoing and should allow to better understand the role of transcription factors, other than Bicoid, in hunchback expression at the mechanistic level. Thesis Doctor of Philosophy (PhD) Have you ever wondered how a single cell can become a full grown organism? Well it starts when an egg and sperm fuse together. As time passes this single cell divides over and over again until an organism is formed. During this developmental process, somehow the cells know exactly where they are and what they need to become so that they form the organism. However, we don’t fully understand this process and this is what we hope to answer with our research: How do the cells know where they are and what they need to become during development? We study this process in the fruit fly. Although fruit flies might not look a lot like us, during early embryonic development we are quite similar, so we can try to answer these questions in fruit flies and what we find might be relevant to other organisms like us. During development, the first element that an embryo needs to know is the orientation of its body, where the head and tail, the left and right and the back and front of the body will be. We concentrate on studying how the head to tail axis, which we call the anterior-posterior axis, is formed. To know where the head is going to be, the embryo releases proteins called morphogens that broadcast instructions to other genes so that cells know where they are and what they should become. We study a morphogen called Bicoid. Its concentration is high in the anterior, the region that will become the head of the embryo, and lower as you move towards the posterior where the tail will form. Bicoid activates a gene called hunchback, which ends up dividing the embryo in two large parts, the top and the bottom. However, Bicoid’s message fades away during each cell division and needs to be read again at the beginning of each new nuclear cycle. So how is the message read and how long does this process take? This last question is particularly critical during the period of very fast cell division. My thesis tries to answer this question. We found out that it takes 3 minutes for a nuclei to read the Bicoid concentration, activate hunchback and express it correctly. However, in contrast to what was believed before, or namely, that only Bicoid was involved in this process, we found out that other players are involved in helping relay this message. This way hunchback can accurately divide the body in two parts exactly in the middle and without mistake in such a short period of time.

With the global effort to “green” the transportation industry, the internal combustion engine (ICE) is required to reduce its carbon footprint and can do so by increasing efficiency. Plasma Electrolytic Oxidation (PEO) coatings confer properties of high wear and corrosion resistance, high hardness, excellent adhesion and a superior thermal barrier to their substrate, suggesting that coating of ICE components has the potential to greatly improve the combustion efficiency. The primary objective of this thesis is to analyze the effects of PEO coating on the surface of pistons in a spark ignition ICE, with the goal of increasing efficiency, to address the industry targets of reduced carbon emissions and increase fuel efficiency. Testing was conducted on a 7.3L Ford engine fitted with stock Al alloy pistons, followed by in-house PEO-coated pistons. An AC Medien dynamometer and a A&D Pheonix AM/RT combustion analysis system using real-time in-cylinder pressure data, coupled with the dynamometer cell data were used. The testing was built off real world driving cycles overlaid on a brake specific fuel consumption (bsfc) map, to choose areas upon which to focus during the completion of the spark and VCT timing sweeps at both, low load and high load conditions. Compared to stock pistons, the PEO-coated pistons modestly improved bsfc, thermal efficiency, increased HRR and bulk gas temperature at low load, low speed conditions. These benefits did not project further onto retarded VCT angles and higher speeds, where PEO-coated pistons did not change thermal efficiency, bfsc, had minimal impact on bulk gas temperatures, and minimally influenced the IMEP COV. At high load and speed conditions the PEO-coating negatively impacted the engine performance, inducing knock. An increased fuel enrichment was required to reduce exhaust temperature limits, since retarding spark was required to reduce knocking. The engine could not be run at optimum efficiency or MBT spark timing, decreasing the bsfc and thermal efficiency. In conclusion, although benefit derived from the use of PEO-coated pistons was observed only in limited engine conditions, the data collected and analyzed in this thesis provides a direction for future study to continue to explore the potential advantages of PEO-coated pistons.

Recently, graph-structured data has become increasingly developed in a variety of fields from biological networks to social networks. While link prediction is one of the key problems in graph theory, cell-cell communication regulates individual cell activities and is a crucial part of tissue structure and function. In this regard, recent advances in single-cell RNA sequencing technologies have eased routine analyses of intercellular signaling networks. Previous studies work on various link prediction approaches. These approaches have certain assumptions about when nodes are likely to interact, thus, showing high performance for some specific networks. Subgraph-based methods have solved this problem and outperformed other approaches by extracting local subgraphs from a given network. In this work, we present a novel method, called Subgraph Embedding of Gene expression matrix for prediction of CEll-cell COmmunication (SEGCECO), which uses an attributed graph convolutional neural network to predict cell-cell communication from single-cell RNA-seq data. SEGCECO captures the latent as well as explicit attributes of undirected, attributed graphs constructed from the gene expression profiles of individual cells. High-dimensional and sparse single-cell RNA-seq data make the process of converting the data to a graphical format a daunting task. We successfully overcome this limitation by applying SoptSC, a similarity-based optimization method in which the cell-cell similarity matrix is learned from single-cell gene expression data. The cell-cell communication network is then built using this similarity matrix. To evaluate our proposed method, we performed experiments on six scRNAseq datasets extracted from the human and mouse pancreas tissue. Our comparative analysis shows that SEGCECO outperforms latent feature-based approaches, as well as the state-of-the-art method for link prediction, WLNM, with 0.99 ROC area under the curve and 99% prediction accuracy.