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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
Examination of Listeria monocytogenes survival in refrigerated chopped hard-boiled eggs and deli salads containing this ingredient
No full textPeeled hard-boiled eggs (HBEs) are widely favored by both consumers and food services due to their convenience. These HBEs are often chopped and incorporated into
various dishes such as deli salads. However, recent recalls of hard-boiled eggs have brought
attention to the risk of contamination with Listeria monocytogenes. Prepared HBEs are
typically subjected to antibacterial treatment to maintain product safety and quality. Citric
acid is a common antibacterial used in the food industry to treat the HBEs. Previous
research has determined that 2% citric acid treatment is effective against L. monocytogenes
on whole HBEs. This study examined the efficacy of citric acid on the reduction of L.
monocytogenes on chopped HBEs and in deli salads containing chopped HBEs. HBEs were
treated with 2% citric acid or water (untreated) by submersion for 24 h at 5°C. HBEs were
dried for 10 min, inoculated with a 4-strain cocktail of rifampicin-resistant L.
monocytogenes, at 1 (low-level inoculation) or 4 log CFU/HBE (high level-inoculation),
and allowed to dry for 10 min. Low-level inoculated HBEs were chopped and stored at 5,
10, or 15°C for 28 d. High-level inoculated HBEs were chopped and stored at 5, 10, and
25°C for 14 d. Low-level inoculated HBEs were also chopped and incorporated into potato,
tuna, chicken, or macaroni salad at a 1:6 ratio (HBE to other ingredients), or into egg salad
at a 7:1 ratio. Salads were stored at 5, 10, or 15°C for 28 d. The presence of L.
monocytogenes was determined at intervals during storage by enrichment with BLEB
and/or enumerated on BHIArif throughout storage. Triplicate samples were assessed for
each time point, and three independent trials were conducted. Data was analyzed by
Student’s T-test, ANOVA, and Fisher’s exact test, p≤0.05. For low-level inoculated
chopped HBEs, the L. monocytogenes population was significantly higher in untreated chopped HBEs (1.86±0.33 log CFU/g) as compared to treated chopped HBEs (1.47±0.27
log CFU/g) on day 14 at 15°C. On both untreated and treated chopped HBEs, there was no
significant difference in the population of L. monocytogenes up to 7 d. However, from 14
d, there was a significant increase in the growth of L. monocytogenes (1.86±0.33 to
2.18±0.35 log CFU/g on untreated chopped HBEs and 1.47±0.27 to 1.94±0.47 log CFU/g
for treated, respectively). For high-level inoculated HBEs, a higher L. monocytogenes
growth rate was observed on untreated chopped HBEs as compared to treated chopped
HBEs at 10 and 25°C. It was observed that treated chopped HBEs at 5°C took the longest
to reach 1 log CFU/g increase in the L. monocytogenes population (50 d) whereas,
untreated chopped HBEs at 25°C took the shortest (0.22 d). Untreated chopped HBEs
showed a significantly higher population of L. monocytogenes as compared to treated
chopped HBEs on 14 d at all storage temperatures. In deli salads containing chopped HBEs,
potato salad showed the highest growth of L. monocytogenes after 14 d, followed by
macaroni, egg, chicken, and tuna salad. The population of L. monocytogenes was the lowest
in tuna salad. L. monocytogenes was present throughout the storage period at all storage
temperatures. It was observed that 2% citric acid is more efficient in controlling the growth
of L. monocytogenes in chopped HBEs as compared to when those HBEs are incorporated
into deli salads. The findings contribute to the formulation of preventive measures and
standards aimed at guaranteeing the safety of HBEs
The Voderettes: Gender, Labor, and Techno-Utopia at the 1939 New York World's Fair
No full textThis thesis explores the labor demands of the Voder, the electrical speech synthesis machine developed by Bell Labs to be a major component of AT&T's 1939 New York World's Fair exhibit. With the United States emerging from the Great Depression, and with political tensions escalating across the globe, the paper situates the Voder's labor demands within the historical context of the fair. Specifically, I explore the decision to have young women operate the Voder, the intricacies of the machine cloaked by the warm presence of its highly-skilled female operator. Using archival records from Bell Labs engineers, the paper exposes the previously unacknowledged engineering contributions of Voder operators in the years before the fair. These young women not only influenced major decisions about the Voder's mechanics but also gave early credence to the notion that developing a performance with the machine could make for a thrilling fair exhibit. Moreover, the paper argues that at the fair itself, AT&T and Bell Labs executives used the Voder operators to normalize a new vision of a technological utopia that relied heavily and conspicuously on the infrastructural labor of women. Given the Voder's legacy, as a tool that laid critical groundwork for voice encryption technology, the paper adds important context to the historical record, highlighting the young women at the heart of the machine
Using Niobium surface encapsulation and Rhenium to enhance the coherence of superconducting devices
No full textIn recent decades, the scientific community has grappled with escalating complexity, necessitating a more advanced tool capable of tackling increasingly intricate simulations beyond the capabilities of classical computers. This tool, known as a quantum computer, features processors composed of individual units termed qubits. While various methods exist for constructing qubits, superconducting circuits have emerged as a leading approach, owing to their parallels with semiconductor technology.In recent years, significant strides have been made in optimizing the geometry and design of qubits. However, the current bottleneck in the performance of superconducting qubits lies in the presence of defects and impurities within the materials used. Niobium, owing to its desirable properties, such as high critical temperature and low kinetic inductance, stands out as the most prevalent superconducting material. Nonetheless, it is encumbered by a relatively thick oxide layer (approximately 5 nm) exhibiting three distinct oxidation states: NbO, NbO, and NbO. The primary challenge with niobium lies in the multitude of defects localized within the highly disordered NbO layer and at the interfaces between the different oxides. In this study, I present an encapsulation strategy aimed at restraining surface oxide growth by depositing a thin layer (5 to 10 nm) of another material in vacuum atop the Nb thin film. This approach exploits the superconducting proximity effect, and it was successfully employed in the development of Josephson junction devices on Nb during the 1980s.In the past two years, tantalum and titanium nitride have emerged as promising alternative materials, with breakthrough qubit publications showcasing coherence times five to ten times superior to those achieved in Nb. The focus will be on the fabrication and RF testing of Nb-based qubits with Ta and Au capping layers. With Ta capping, we have achieved the best T1 (not average) decay time of nearly 600 us, which is more than a factor of 10 improvements over the bare Nb. This establishes the unique capping layer approach as a significant new direction for the development of superconducting qubits.Concurrently with the exploration of materials for encapsulation strategies, identifying materials conducive to enhancing the performance of superconducting qubits is imperative. Ideal candidates should exhibit a thin, low-loss surface oxide and establish a clean interface with the substrate, thereby minimizing defects and potential sources of losses. Rhenium, characterized by an extremely thin surface oxide (less than 1 nm) and nearly perfect crystal structure alignment with commonly used substrates such as sapphire, emerges as a promising material platform poised to elevate the performance of superconducting qubits
Evaluation of the efficacy of power ultrasound technology coupled with organic acids to reduce listeria monocytogenes on peaches and apples
No full textFresh fruits and vegetables are prone to microbial contamination throughout different phases of human handling, processing, transportation, and distribution.
Emerging technologies, such as power ultrasound, have received attention due to their
capacity to reduce or eliminate foodborne bacterial pathogens on these commodities.
Power ultrasound, when combined with certain antimicrobials, has demonstrated its
effectiveness as a valuable tool for washing fresh produce. The objective of this study
was to examine the effectiveness of power ultrasound combined with organic acids on the
reduction of Listeria monocytogenes on fruits. In this study, peaches and apples were
subjected to surface inoculation with a four-strain cocktail of L. monocytogenes and dried
for 1 h. Stomacher bags, containing 225 mL of citric, lactic, or malic acids at
concentrations of 1%, 2%, or 5%, were employed for treating inoculated peaches and
apples. The acid treatment was used alone, or in combination with power ultrasound for
2, 5, or 10 min. Water was used for controls. Before treatment, the initial population of L.
monocytogenes on apples was lower compared to the initial population on peaches, with
apples showing a 1.94 log CFU/fruit reduction. Water controls demonstrated no
significant log reduction in both apples and peaches. The greatest L. monocytogenes
reduction on apples occurred when treated with 1% citric acid for 2 min with power
ultrasound where L. monocytogenes was significantly reduced from 6.98±0.88 log
CFU/fruit to 5.56±0.91 log CFU/fruit. The greatest L. monocytogenes reduction on
peaches occurred when treated with 5% citric acid for 5 min with power ultrasound
where L. monocytogenes was significantly reduced from 7.44±0.45 log CFU/fruit to
6.68±0.40 log CFU/fruit.
Overall, the combined effect of acid and power ultrasound was more pronounced
in apples than in peaches. The survival of L. monocytogenes on apples and peaches
appeared to be highly dependent on the specific treatment and hurdle technology applied.
The combination of ultrasound hurdle technology with acid washing has proven effective
in reducing L. monocytogenes on both peaches and apples, with a more significant impact
observed on apples. While acid washing is a more economical option compared to
ultrasound technology, the efficiency of microorganism reduction is considerably
enhanced when power ultrasound is combined with organic acids. Looking ahead, the
development of cost-effective power ultrasound methods could facilitate widespread
adoption of ultrasound hurdle technology in the produce industry
Workplace Incivility and Work- and Health-Related Outcomes: The Role of Job Embeddedness
No full textJob embeddedness has been widely studied to understand why people stay at their organization, yet recently, research has started to examine the so-called “dark side” of job embeddedness. Drawing from Conservation of Resource (COR) theory, this study extends research on the potential “dark side” of job embeddedness, particularly examining its relationship between workplace incivility and turnover intentions, emotional exhaustion, and physical health symptoms. Contrary to the initial hypotheses, results from the study (N = 395) indicated that on-the-job embeddedness moderated (strengthened) the positive relationship between workplace incivility and turnover intentions. As expected, on-the-job embeddedness moderated (strengthened) the positive relationship between workplace incivility and emotional exhaustion, but it did not moderate the relationship with physical health symptoms. This study contributes to the workplace mistreatment and job embeddedness literature as it explored when and how on-the-job embeddedness and its components (i.e., links, fit, sacrifice), as well as off-the-job embeddedness influence the relationships between workplace incivility and work- and health-related outcomes. The discussion explores the broader implications of on-the-job embeddedness and workplace incivility, highlighting important considerations for both researchers and practitioners in managing employee retention and well-being
Defense-in-Depth for Cyber-Secure Network Architectures of Industrial Control Systems
No full textDigitization and modernization efforts have yielded greater efficiency, safety, and cost-savings for Industrial Control Systems (ICS). To achieve these gains, the Internet of Things (IoT) has become an integral component of network infrastructures. However, integrating embedded devices expands the network footprint and softens cyberattack resilience. Additionally, legacy devices and improper security configurations are weak points for ICS networks. As a result, ICSs are a valuable target for hackers searching for monetary gains or planning to cause destruction and chaos. Furthermore, recent attacks demonstrate a heightened understanding of ICS network configurations within hacking communities. A Defense-in-Depth strategy is the solution to these threats, applying multiple security layers to detect, interrupt, and prevent cyber threats before they cause damage. Our solution detects threats by deploying an Enhanced Data Historian for Detecting Cyberattacks. By introducing Machine Learning (ML), we enhance cyberattack detection by fusing network traffic and sensor data. Two computing models are examined: 1) a distributed computing model and 2) a localized computing model. The distributed computing model is powered by Apache Spark, introducing redundancy for detecting cyberattacks. In contrast, the localized computing model relies on a network traffic visualization methodology for efficiently detecting cyberattacks with a Convolutional Neural Network. These applications are effective in detecting cyberattacks with nearly 100% accuracy.
Next, we prevent eavesdropping by applying Homomorphic Encryption for Secure Computing. HE cryptosystems are a unique family of public key algorithms that permit operations on encrypted data without revealing the underlying information. Through the Microsoft SEAL implementation of the CKKS algorithm, we explored the challenges of introducing Homomorphic Encryption to real-world applications. Despite these challenges, we implemented two ML models: 1) a Neural Network and 2) Principal Component Analysis.
Finally, we hinder attackers by integrating a Cyberattack Lockdown Network with Secure Ultrasonic Communication. When a cyberattack is detected, communication for safety-critical elements is redirected through an ultrasonic communication channel, establishing physical network segmentation with compromised devices. We present proof-of-concept work in transmitting video via ultrasonic communication over an Aluminum Rectangular Bar. Within industrial environments, existing piping infrastructure presents an optimal solution for cost-effectively preventing eavesdropping. The effectiveness of these solutions is discussed within the scope of the nuclear industry
Capital Design: The Role of Design in Institutional Capital Allocation
No full textThere is a paradox within the $100 trillion institutional investment industry: the more choices an institutional investor has, the more challenging it becomes to make investment decisions. This paradox is significant because capital is one of the most transformational elements of the 21st century, driven by financialization, universal ownership, and increasing systemic risks. The direction of capital flows significantly influences the approach to addressing climate change, aging populations, and the transition to sustainable energy, in addition to supporting the essential physical and social infrastructure supported by institutional capital. This research proposes and substantiates a novel hypothesis: design can significantly influence capital allocation in institutional investment contexts. Through an institutional case study, expert interviews, workshops with master’s level design students, and systems-informed reflective practice, this research identifies asset classes as an important and changeable lens through which institutions engage with the future. It explores how these asset classes shape choices in the capital allocation process and identifies eight design capabilities particularly suited for institutional investment contexts. In doing so, it introduces a framework termed Capital Design. This framework illustrates how design can influence institutional capital allocation by integrating these design capabilities with investment tools through informational lenses within a choice/knowledge map. As a result, Capital Design offers an innovative approach for investors and investees to reorient toward emergent asset categories that directly meet the most urgent societal needs
Prediction and Control of In-Cylinder Processes in Heavy-Duty Engines Using Alternative Fuels
No full textThis Ph.D. thesis focuses on advancing diagnostic techniques and control-oriented models to enhance the efficiency and performance of internal combustion (IC) engines, particularly heavy-duty engines utilizing alternative fuels. The research endeavors to contribute to the field of model-based control of engines through the development and implementation of innovative methodologies. The primary emphasis is on the development of diagnostic methods, control-oriented models and advanced control strategies for compression ignition engines using alternative fuels. The first key topic explores the determination of the Most Representative Cycle for Combustion Phasing Estimation based on cylinder pressure measurements. The method developed extracts crucial information from experimental data obtained from four distinct engines: the heavy-duty single-cylinder GCI engine, the light-duty multi-cylinder diesel engine, a CFR engine, and a single-cylinder light-duty Spark Ignition (SI) engine. This work lays the foundation for precise combustion phasing estimation, a critical parameter for engine control. The second major contribution involves the development of control-oriented models for Variable Geometry Turbochargers (VGT) and inter-coolers. Two models are established: a data-driven turbocharger model and an empirical inter-cooler model. These models are meticulously calibrated and validated using experimental data from a multi-cylinder light-duty diesel engine, providing valuable insights into the behavior of these components under varying conditions. The outcomes contribute to facilitate predictive control of engine air systems. The third core aspect of the thesis revolves around Model Predictive Control of Combustion Phasing in heavy-duty compression-ignition engines utilizing alternative fuels. A combustion phasing and engine load model is derived from experimental data and incorporated into an MPC framework. The MPC strategy is subsequently tested in the heavy-duty GCI test cell and compared against a conventional Proportional-Integral-Derivative (PID) control strategy. The results showcase the effectiveness of the MPC approach in achieving precise control of combustion phasing, demonstrating its potential for optimizing engine performance.
In summary, this Ph.D. thesis contributes significantly to the field of engine controls by advancing diagnostic techniques, control-oriented models, and implementing a cutting-edge MPC-based control strategy for compression ignition engines using alternative fuels. The research findings not only enhance the understanding of in-cylinder processes but also pave the way for more efficient and sustainable heavy-duty engines using alternative fuels
The Voderettes: Gender, Labor, and Techno-Utopia at the 1939 New York World's Fair
No full textThis thesis explores the labor demands of the Voder, the electrical speech synthesis machine developed by Bell Labs to be a major component of AT&T's 1939 New York World's Fair exhibit. With the United States emerging from the Great Depression, and with political tensions escalating across the globe, the paper situates the Voder's labor demands within the historical context of the fair. Specifically, I explore the decision to have young women operate the Voder, the intricacies of the machine cloaked by the warm presence of its highly-skilled female operator. Using archival records from Bell Labs engineers, the paper exposes the previously unacknowledged engineering contributions of Voder operators in the years before the fair. These young women not only influenced major decisions about the Voder's mechanics but also gave early credence to the notion that developing a performance with the machine could make for a thrilling fair exhibit. Moreover, the paper argues that at the fair itself, AT&T and Bell Labs executives used the Voder operators to normalize a new vision of a technological utopia that relied heavily and conspicuously on the infrastructural labor of women. Given the Voder's legacy, as a tool that laid critical groundwork for voice encryption technology, the paper adds important context to the historical record, highlighting the young women at the heart of the machine