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Evaluating antimicrobial efficacy of GS-2 on reusable food packaging materials
No full textPackaging plays an important role in maintaining the quality and safety of fresh produce throughout storage, transportation and end-use by consumers. Single-use packaging poses several environmental impacts; therefore use of reusable packaging is being encouraged in the fresh produce supply chain. However, the utilization of harmful chemicals and inadequate sanitation standards limit the reuse of packaging materials. To overcome these limitations, this study focuses on testing a non-toxic, water-soluble antimicrobial; GS-2 coating to facilitate the reuse of food packaging and reduce the risk of microbial contamination. In this study, the antimicrobial activity of GS-2 was evaluated against foodborne pathogens; Escherichia coli, Listeria monocytogenes and Salmonella enterica on plastic and cardboard coupons at 1 h and 15 min treatment times and 0.3%, 1% and 3% concentration. These coupons were also stored at 4℃ and 90% R.H. and 18℃ and 45% R.H. inoculated on different days up to 42 d with E. coli or L. monocytogenes to study retention of activity of GS-2. Additionally, the efficacy of GS-2 to reduce transfer of bacteria from cardboard and plastic to tomato was investigated. The initial level of inoculum was 9 log CFU/surface for all experiments. Cardboard and plastic without GS-2 were used to compare the reduction of bacteria on the treated surfaces. The differences in the population of bacteria were evaluated using Student’s T-Test and ANOVA; p 4.50 log CFU/surface reduction of all three bacteria in 1 h. There was a lower reduction of the population on cardboard as compared to plastic for all bacteria, the reduction obtained was 1.83, 2.65 and 3.42 log CFU/surface for E. coli, L. monocytogenes and S. enterica, respectively, in 1 h. There was no significant difference between 15 min and 1 h treatments for cardboard. Further, the highest reduction of bacteria was obtained with 3% GS-2 on plastic. For cardboard, no significant difference in population reduction was obtained for E. coli or S. enterica, with 1% or 3% GS-2. However, for L. monocytogenes there was a higher reduction with 3%. GS-2 remained active on the surface of plastic and cardboard for a period of six weeks. For cardboard, there was a lower reduction of bacteria and there was no trend in the population reduction from 0 to 42 d, with the populations remaining within a range of 4-5 log CFU/surface. There was a significant transfer of E. coli or L. monocytogenes from plastic surfaces without GS-2 to tomato at 5-6 log CFU/tomato. However, the transfer of bacteria from the GS-2-coated plastic to the tomato was below the limit of enumeration. For cardboard, the population was below the limit of enumeration, irrespective of the GS-2 coating. Based on the results, GS-2 is a promising antimicrobial that reduces the microbial load on packaging surfaces and prevents cross-contamination of fresh produce. The retention of GS-2 activity makes it suitable for reusable packaging applications
Large Language Model Based Machine Learning Techniques for Fake News Detection
No full textWith advanced technology, it’s widely recognized that everyone owns one or more personal devices. Consequently, people are evolving into content creators on social media or the streaming platforms sharing their personal ideas regardless of their education or expertise level. Distinguishing fake news is becoming increasingly crucial. However, the recent research only presents comparisons of detecting fake news between one or more models across different datasets. In this work, we applied Natural Language Processing (NLP) techniques with Naïve Bayes and DistilBERT machine learning method combing and augmenting four datasets. The results show that the balanced accuracy is higher than the average in the recent studies. This suggests that our approach holds for improving fake news detection in the era of widespread content creation
Utilizing Concurrent Data Accesses for Data-Driven and AI Applications
No full textIn the evolving landscape of data-driven and AI applications, the imperative for reducing data access delay has never been more critical, especially as these applications increasingly underpin modern daily life. Traditionally, architectural optimizations in computing systems have concentrated on data locality, utilizing temporal and spatial locality to enhance data access performance by maximizing data and data block reuse. However, as poor locality is a common characteristic of data-driven and AI applications, utilizing data access concurrency emerges as a promising avenue to optimize the performance of evolving data-driven and AI application workloads.This dissertation advocates utilizing concurrent data accesses to enhance performance in data-driven and AI applications, addressing a significant research gap in the integration of data concurrency for performance improvement. It introduces a suite of innovative case studies, including a prefetching framework that dynamically adjusts aggressiveness based on data concurrency, a cache partitioning framework that balances application demands with concurrency, a concurrency-aware cache management framework to reduce costly cache misses, a holistic cache management framework that considers both data locality and concurrency to fine-tune decisions, and an accelerator design for sparse matrix multiplication that optimizes adaptive execution flow and incorporates concurrency-aware cache optimizations.Our comprehensive evaluations demonstrate that the implemented concurrency-aware frameworks significantly enhance the performance of data-driven and AI applications by leveraging data access concurrency.Specifically, our prefetch framework boosts performance by 17.3%, our cache partitioning framework surpasses locality-based approaches by 15.5%, and our cache management framework achieves a 10.3% performance increase over prior works. Furthermore, our holistic cache management framework enhances performance further, achieving a 13.7% speedup. Additionally, our sparse matrix multiplication accelerator outperforms existing accelerators by a factor of 2.1.As optimizing data locality in data-driven and AI applications becomes increasingly challenging, this dissertation demonstrates that utilizing concurrency can still yield significant performance enhancements, offering new insights and actionable examples for the field. This dissertation not only bridges the identified research gap but also establishes a foundation for further exploration of the full potential of concurrency in data-driven and AI applications and architectures, aiming at fulfilling the evolving performance demands of modern and future computing systems
Extremal and Enumerative Problems on DP-Coloring of Graphs
No full textGraph coloring is the mathematical model for studying problems related to conflict-free allocation of resources. DP-coloring (also known as correspondence coloring) of graphs is a vast generalization of classic graph coloring, and many more concepts of colorings studied in the past 150+ years.
We study problems in DP-coloring of graphs that combine questions and ideas from extremal, structural, probabilistic, and enumerative aspects of graph coloring. In particular, we study (i) DP-coloring Cartesian products of graphs using the DP-color function, the DP coloring counterpart of the Chromatic polynomial, and robust criticality, a new notion of graph criticality; (ii) Shameful conjecture on the mean number of colors used in a graph coloring, in the context of list coloring and DP-coloring; and (iii) asymptotic bounds on the difference between the chromatic polynomial and the DP color function, as well as the difference between the dual DP color function and the chromatic polynomial, in terms of the cycle structure of a graph. These results respectively give an upper bound and a lower bound on the chromatic polynomial in terms of DP colorings of a graph
Large-Signal Transient Stability and Control of Inverter-Based Resources
No full textRenewable generation, including solar photovoltaic (PV) systems, type 3 and 4 wind turbine generation systems (WTG), battery energy storage systems (BESS), as well as high voltage direct current (HVDC) and flexible alternating current (FACT) transmission system devices with increasing penetration level are being connected to the bulk power systems (BPS) via power electronic (PE) converters as the interface, referred to as the inverter-based resources (IBRs) on the transmission and sub-transmission levels or distributed energy resources (DERs) located on the distribution level. The IBR is almost entirely defined by the control algorithms and found to be more prone to experiencing large disturbances due to the lack of the conventional synchronous machine (SM) intrinsic synchronous characteristics and mechanical inertia, as well as being in smaller capacity sizes. Thus, these reasons motivate this dissertation to study the large-signal transient stability and control of IBRs for reliable grid integration and rapid grid transformation. For large-signal stability analysis methods, Lyapunov-based methods are the fundamental theory used to characterize the stability issues with analytical solutions, although other non-Lyapunov methods could also be very helpful. A main difficulty hindering the widespread adoption of the Lyapunov stability analysis method is the difficulty of finding the proper Lyapunov function candidate for a higher dimensional nonlinear system. The Port-Hamiltonian (PH) nonlinear control theory is explored in this dissertation as a promising theoretical framework solution addressing this challenging issue. A PH-based tracking and robust control method is proposed to facilitate the practical application of the PH framework in IBR controls. In addition, considering the typical grid-forming (GFM) IBR control with a first-order low pass filter (LPF) block is usually involved with control saturation function for protection purposes under abnormal operating conditions with anti-windup issue in practical implementation, a PH-based bounded LPF (PH-BLPF) control is proposed to incorporate this in the large-signal PH interconnection modeling framework while preserving the robust tracking Lyapunov stability with improved transient dynamic performance and stability margin.Moreover, specific real-world transient synchronization stability issues, such as the grid voltage large fault disturbance case, are studied. In addition, to meet the recent emerging IBR grid code requirements, such as the current magnitude limitation, grid support function, and fault recovery capability of GFM-VSCs, a virtual impedance-based current-limiting GFM control with enhanced transient stability and grid support is proposed
Two Essays on Mergers and Acquisitions
No full textThis dissertation is composed of two self-contained chapters that both relate to mergers and acquisitions (M&A). In the first essay, we examine the Delaware (DE) reincorporation effect on firms’ post-IPO behaviors on mergers and acquisitions. We find that firms’ DE reincorporation decisions enhance the likelihood of engaging in M&A as targets. However, as a tradeoff, DE reincorporated firms get lower takeover valuations compared to stay-at-home-state firms, and the acquisition of reincorporated firms is less likely to be successful. Our second essay aims to explore the role of the options market in price discovery for M&A. We find that the predictive power of the changes in implied volatility of the target firm stock for the takeover outcome is statistically and economically significant. The risk arbitrage portfolios incorporating filters derived from the options on stocks of the target firms generate annualized risk-adjusted abnormal returns between 2.6% and 5%, depending on the portfolio weighting method, the threshold of filters for the implied volatility change, and the asset pricing models applied for abnormal returns. The results are robust to different empirical setups and are not explained by traditional factors
Nanopore sensing for environmental and biomarker analysis
No full textNanopore stochastic sensing is a powerful analytical tool for detecting target molecules through a nanoscale pore. The analyte and electrolyte ions are subjected to a voltage bias which drives them to translocate through the nanopore, resulting in disruptions in the ionic current. These disruptions are translated to blockage events which can serve as a signature of the analyte. Owing to its unique features of single-molecule and label-free sensing, nanopore technique has been exploited in a wide array of applications such as detection of metal ions, proteins, DNA, microRNA, toxic agents etc. In this dissertation, projects showcasing nanopore’s sensing capability of different biomarkers and in the detection of a wide range of target molecules based on non-covalent interactions are presented. Particularly in the first two projects, nanopore detection of ferric ions relevant to environmental regulation as well as a biomarker for human health and a miRNA-based biomarker for oral cancer and oral related diseases are summarized. Ferric ions, which are benign if present in balanced quantities but can be toxic otherwise, are detected by using an engineered multifunctional nanopore and a chelating organophosphonic acid ligand. The chelate complex formed after ferric ions bind to ligand gives significantly different event signatures than the free ligand in the solution enabling ferric ion detection. Even in the presence of interfering ions, the ferric ions could be recognized easily because of the conformational changes brought in the nanopore lumen by the interaction of the interfering metal ions with the His-tags of the nanopore which in turn resulted in variations in the characteristics of blocking events. In the second project, miR31, an oral cancer biomarker, is selectively detected with the help of an engineered nanopore, and a DNA based probe. Several probes with variations in length, composition and position of the overhangs or probes with no overhangs were compared and studied as the probes play a crucial role in capturing the target of interest with high specificity. Our strategically designed probe emerged as the most effective in capturing the target even in presence of large background from human saliva samples and enhanced the sensitivity of the system. In the first two projects, nanopores are utilized for selective and specific detection of certain target molecules. However, in order to analyze diverse range of analytes, numerous sensing systems have to be constructed which can be a time-consuming and challenging task. To circumvent this limitation, in the third project, diverse recognition sites based on various non-covalent interactions are incorporated into the α-hemolysin protein pore to achieve detection of not just a single analyte but broad category of molecules such as cations, anions, aromatic and hydrophobic compounds
Optimization of Large-Scale NOMA With Incidence Matrix Design and Physical Layer Security
No full textThe Non-Orthogonal Multiple Access (NOMA) system is recognized for its capability to achieve higher spectral efficiency and massive connectivity. NOMA is intended to transmit massive user communications. The incidence matrix governs the relationship between users and resources for the Code domain NOMA (CD-NOMA). However, NOMA studies focus less on the design and optimization of the incidence matrix.Therefore, this thesis aims to investigate the development of a secure and large-scale NOMA system based on incidence matrix design. The main contributions are outlined as follows:
Firstly, this research introduces a novel NOMA system. Distinct from existing studies, the NOMA system is based on combinatorial design. This innovative approach, coupled with a unique constellation design, eliminates the surjective mapping from the linear adding data of multiusers, reducing the complexity of constellation design and Multiuser Detection (MUD). The characteristics of the incidence matrix designs, Simple Orthogonal Multi-Arrays (SOMA), are explored, which display a distinct Latin Square pattern. The SOMA design's unique structure allows for the creation of a highly flexible and fair resource allocation matrix. The NOMA system's theoretical performance analysis equations are established, supporting dynamic adaptability and optimization. The design is validated by Monte Carlo simulation. Compared to other NOMA schemes, it offers higher degrees of freedom and lower complexity while maintaining graceful error rates to transmit a larger number of users.
Secondly, a novel NOMA system utilizing incidence matrix information in the uplink is investigated. The incidence matrix pattern is exploited for MUD to achieve large-scale user connectivity. The incidence matrix is designed based on two critical mathematical concepts: parallel classes in hypergraph theory and orthogonal arrays (OAs) in combinatorial designs. Unlike other NOMA schemes, which require modification of their receiver and transmitter to decode superimposed multiuser signals, the unique pattern of the OA structure enables the use of conventional modulators. Consequently, the system load increases and the complexity and latency are reduced. The order of magnitude of the decoding complexity can be significantly reduced from O(N^3) to O(N) compared to the conventional minimum mean-square estimation (MMSE) decoder. Monte Carlo simulation validates that this novel NOMA system outperforms other NOMA designs in terms of error rate, data rate, and system size.
Finally, a reconfigurable convolutional encoder design that integrates security and error correction based on physical layer security (PLS) and randomness is developed. This design addresses concerns over privacy, security, and reliability of Internet of Things devices in edge computing networks. The lightweight Convolutional encoders are designed to ensure security by updating the transfer function dynamically with user data. The reconfigurability of the design is achieved by replacing the fixed adder that represents the generator polynomials with the switch adder, enabling the use of 87 billion distinct updating structures, thereby enhancing the versatility of the design. BER-based PLS paradigms are demonstrated in the simulation. In the simulation, the robustness and randomness of this design are further validated through tests suggested by the National Institute of Standards and Technology for cryptographically secure pseudorandom number generators, such as the monobits, longest one, and run tests
Modeling and Optimization of Embedded Active Flow Control Systems
No full textThis thesis presents research focused on the aerodynamic performance of circulation control on two-dimensional and quasi-two-dimensional wings. Aerodynamic loads, namely lift, drag, and moment coefficients, are measured through Reynolds Averaged Navier Stokes (RANS) modeling and wind tunnel experiment. A simplified and parameterized RANS model is presented as a rapidly iterable approach to estimating the performance of trailing-edge circulation control on two dimensional airfoils, with the hypothesis that an optimized airfoil shape can be found which maximizes the lift coefficient increment generated by circulation control, through modification of the wing profile. The simplified modeling setup is compared with more conventional approaches to numerical simulation of circulation control. The performance of the simplified modeling scheme is then compared with wind tunnel studies, for both steady-state and dynamic performance, as functions of both momentum coefficient dCμ and chord-based Reynolds number Re_c. The dynamic performance for the model is studied to find an analog to the theoretical unsteady models of Wagner and Theodorsen. An adjoint optimization framework is used to find an optimal airfoil profile for circulation control. The optimized profile is then compared in both a simulation and a wind tunnel test study against a NACA0015 airfoil. In simulation, improvement between 12% and 15% is seen for the lift control authority for all values of dCμ and Re_c tested. In experiment, the optimized profile demonstrated improvements of up to 28% in lift control authority, dCL/dCμfor values of Cμ, and decreased performance for higher values of Cμ
Resolvent Analysis of Turbulent Flow over Compliant Surfaces: Optimization Methods and Stability Considerations.
No full textThis thesis delves into the manipulation of turbulence properties through innovative compliant surface designs. Turbulence, known for its unpredictable fluid movements, presents substantial challenges across engineering disciplines, particularly in optimizing system efficiency and minimizing energy losses. This research explores the potential of compliant surfaces to control and mitigate the adverse effects of turbulent flow, thereby enhancing the performance and reliability of engineering systems.Employing the resolvent analysis method, this work investigates the interaction between turbulent flows and surfaces capable of dynamic adaptation. The study evaluates the impact of these surfaces on turbulence suppression through the application of both space-dependent and independent compliance models, where the compliance model is characterised by an admittance, which represents the relationship between the instantaneous surface pressure and surface velocity. This approach allows for a nuanced understanding of how different surface properties can influence the behavior of turbulent flows.A significant contribution of this thesis is the comprehensive stability analysis conducted to assess the implications of compliant surfaces on the linear stability of the dynamical system. By examining the eigenvalues of the mean-linearized system, the research identifies the conditions under which compliant surfaces may induce or mitigate instabilities within turbulent flows. This analysis is pivotal in developing compliant surface designs that not only reduce turbulence-induced energy losses but also ensure the stability of the flow, a critical consideration for practical engineering applications.The findings of this thesis offer valuable insights into the role of surface compliance in turbulence control, paving the way for further research and the development of advanced engineering solutions. Through a detailed investigation of the interactions between compliant surfaces and turbulent flows, this work contributes to the broader field of fluid dynamics and underscores the potential of innovative surface designs in achieving more efficient and sustainable engineering systems