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The Double-edged Sword of Executive Pay: How the CEO-TMT Pay Gap Influences Firm Performance
No full textThis study examines the relationship between the chief executive officer (CEO) and top management team (TMT) pay gap and consequent firm performance. Drawing on tournament theory and equity theory, I argue that the effect of the CEO-TMT pay gap on consequent firm performance is non-monotonic. Using data from 1995 to 2022 from S&P 1500 US firms, I explicate an inverted U-shaped relationship, such that an increase in the pay gap leads to an increase in firm performance up to a certain point, after which it declines. Additionally, multilevel analyses reveal that this curvilinear relationship is moderated by attributes of the TMT, and the industry in which the firm competes. My findings show that firms with higher TMT gender diversity suffer lower performance loss due to wider pay gaps. Furthermore, when firm executives are paid more compared to the industry norms, or when the firm has a long-tenured CEO, firm performance becomes less sensitive to larger CEO-TMT pay gaps. Lastly, when the firm competes in a masculine industry, firm performance is more negatively affected by larger CEO-TMT pay gaps. Contrary to my expectations, firm gender-diversity friendly policies failed to influence the CEO-TMT pay gap-firm performance relationship
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
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
Multivariable Real-Time Detection of Acute Psychological Stress and Physical Activity in Enhancing the Efficacy of Artificial Pancreas Systems
No full textThe management of Type 1 Diabetes (T1D) requires continuous monitoring and precise control of blood glucose levels, which can be influenced by various physiological factors such as physical activity (PA) and acute psychological stress (APS). This dissertation presents a novel multivariable real-time detection system designed to identify PA and APS, enhancing the efficacy of artificial pancreas (AP) systems. Using data from wearable devices, such as the Empatica E4 wristband, various physiological signals were captured, including blood volume pulse (BVP), accelerometer data (ACC), galvanic skin response (GSR), and skin temperature (ST). These signals were processed to extract features critical for classifying PA and APS.
A Long Short-Term Memory (LSTM) neural network model was employed to classify different types of PA and APS events. Additionally, a multitask learning framework was developed to simultaneously estimate energy expenditure (EE) alongside the classification tasks. The study incorporated explainable artificial intelligence techniques, such as SHAP (Shapley Additive Explanations), to interpret the model’s decisions and ensure that physiologically relevant features were used in the classifications.
A real-time system was implemented, integrating the detection of PA and APS events into an automated insulin delivery (AID) system. This system was validated through real-time testing with participants, demonstrating its ability to respond dynamically to physiological changes and provide timely insulin adjustments. The models achieved high classification accuracy, demonstrating that the integration of PA and APS detection into AP systems can lead to more precise insulin delivery, thereby improving glycemic control in individuals with T1D
Translational Research to Advance Remediation and Label-Free Detection
No full textTranslational research acts as a vital link between fundamental scientific discoveries and their real-world applications, especially within biotechnology and medical diagnostics. This interdisciplinary approach integrates knowledge from biology, chemistry, engineering, and medicine to create inventive solutions for urgent health challenges. Tissue remediation, essential in modern healthcare, seeks to restore the function and vitality of damaged tissues by imitating natural regenerative processes and employing biomaterials and scaffolds. Effective collaboration among academia, industry, and healthcare providers is essential for translating tissue remediation strategies into patient care, offering hope to those with injuries and chronic diseases.One project focuses on crafting biocompatible scaffolds resembling the body's extracellular matrix to facilitate tissue regeneration. This study focuses on enhancing collagen production, particularly for patients with pelvic organ prolapse (POP), using a combination of silk fibers functionalized with carbon nanotubes (SF-CNT) and electrical stimulation (ES). Key findings include superior alignment of SF-CNT fibers compared to pure silk fibers, with SF-CNT 0.1% showing optimal alignment. Higher CNT concentrations led to distorted fibers. SF-CNT 0.1% fibers displayed improved properties and minimal cytotoxicity, while ES increased collagen production, especially with SF-CNT 0.1% fibers. Customized ES conditions based on patient characteristics and tissue locations were crucial for optimal collagen enhancement. In vivo studies showed increased collagen content and improved fiber alignment with ES-treated fibroblast cells. Personalized ES conditions are essential for optimizing collagen enhancement, considering individual patient attributes and tissue-specific factors. The combination of SF-CNT fibers and ES offers promise for improving collagen production, particularly for POP patients. The study highlights the importance of personalized treatment strategies and tissue-specific considerations to maximize the effectiveness of electrical stimulation for collagen enhancement.
Another promising avenue is the shift toward label-free detection methods in medical diagnostics, particularly for conditions like periodontitis. This project introduces an innovative method for rapid, label-free detection of bacterial species linked to periodontitis using Surface-Enhanced Raman Spectroscopy (SERS) combined with machine learning. Key advancements include optimizing saliva processing techniques, and establishing a reliable RT-qPCR method. SERS is utilized for bacterial detection with high accuracy, enhanced by incorporating a cell-free saliva matrix. These methods enable non-invasive, real-time characterization of biomolecular interactions and disease biomarkers, streamlining diagnostic processes and improving accessibility by eliminating the need for labeling agents.
In the pursuit of translational research, collaboration and innovation are paramount. By translating scientific insights into practical solutions, researchers endeavor to advance tissue remediation and label-free detection, thereby contributing to a more sustainable, healthier, and safer future for all
Explaining the Predictions of Image Classifiers
No full textWith the deployment of deep neural network (DNN) models for safety-critical applications such as autonomous driving and medical diagnosis, explaining the decisions of DNNs has become a critical concern. For humans to trust the decision of DNNs, not only the model must perform well on the specified task, it must also generate explanations that are easy to interpret. There is a significant amount of research that investigates the contributions of features, in a given instance, to the model’s prediction, where the contribution constitutes the explanation for the model’s decision. Specifically, in the computer vision domain, the explanation method often generates a saliency map that indicates the importance of the pixels for DNNs to make the prediction from the original input image. I propose explanation approaches that generate better saliency maps that represent the importance of the pixels more accurately and evaluate models’ decision-making reasoning from a human perspective.First, I investigate the source of the noise generated by a well-known explanation method, Integrated Gradient (IG), and its variants. Specifically, I propose the Important Direction Gradient Integration (IDGI) framework, which can be incorporated into all IG-based explanation methods and reduce the noise in their outputs. Additionally, I proposed a novel measurement for assessing the attribution techniques’ quality, i.e., the Accuracy Information Curve (AIC) and the Softmax Information Curve (SIC) using the Multi-scale Structural Similarity Index Measure (MS-SSIM). We show that this metric offers a more precise measurement than the original AIC and SIC. Extensive experiments show that IDGI can drastically improve the quality of saliency maps generated by the underlying IG-based approaches.Second, I introduce Information Propagation, IProp, a novel explanation method that leverages the local structural relationships of pixels. Specifically, IProp considers each pixel as the source of information in a saliency map, and formulates the model explanation through information propagation among pixels. Hence, IProp constructs the saliency map by considering all pixels’ contributions to the prediction jointly. I prove that IProp is guaranteed to converge to the unique solution and is compatible with any existing explanation method. The extensive evaluations show the advantage of applying IProp to the existing explanation methods.In the final chapter, I present a methodology for generating meaningful explanations from a human perspective and evaluate if the model’s rationale agrees with human reasoning. We propose a new framework for evaluating how models make decisions in comparison to humans. We propose a novel evaluation metric to measure the model misalignment with the human decision-making process. We show empirically that complex models have more misalignment with humans than simpler models
Case Study: A Comparison of Pedagogical Content Knowledge Between Coaches and Coaches/Mentees
No full textThis multiple case study dissertation aimed to examine one of the domains of pedagogical content knowledge, knowledge of content and students, between different types of elementary coaches and between coach and their respective collaborating teachers. It also investigated the impact a coaches’ background experiences have on the dynamic between coaches and teachers and the perceptions' teacher have on the effectiveness of coaching. The theoretical framework used in this qualitative study was Ball, Thames, and Phelps’ (2008) definition of PCK. Data was collected from six coaches–four instructional coaches and two math coaches–and eleven k-5th grade teachers. Data collection involved a survey, LMT assessment, and semi-structured interviews, and a thematic analysis method was conducted. The findings from the cross-case analysis resulted in ten themes, with the majority having multiple categories. One finding to one of the research questions was that there were no differences in knowledge of content and students between mathematics coaches and general instructional coaches, but other areas to further investigate emerged. Another finding was that coaches were either within the same capacity as their respective teachers or had extra knowledge of content and students. Although the majority of the coaches’ knowledge of content and students was at a higher level according to their LMT score, it does not necessarily mean that coaches are working with teachers in improving knowledge of content and students. In addition, more research is recommended in creating a pedagogical content knowledge instrument that is specific for coaches
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
Evaluation of the Native Microbiota and Comparative Analysis of a Known Cronobacter Sakazakii and a Newly Isolated Bacillus Cereus Strain in Powdered Infant Formula
No full textThere have been numerous reports of Powdered Infant Formula (PIF) recalls and outbreaks due to the absence of a kill step in the post-pasteurization processing, improper handling pre and post processing and/or reconstitution, and lack of effective sanitization and cleaning of the food contact surfaces in the manufacturing facilities. The objectives of this present study were to 1) survey and identify background microflora in commercial PIF products through microbiological analysis, 16S rRNA, and whole genome sequencing (WGS); 2) compare the survival rate of a known Cronobacter sakazakii and a newly isolated Bacillus cereus DFPST-SP1 in PIF under a humidity level of 33 ± 3% over a period of 28 d; 3) examine the relative resistance of these two strains to thermal treatments at temperatures 40, 70, and 100 °C followed by storage at room temperature (RT) for 30 min; and 4) evaluate the bactericidal effect of 70% ethanol on the two artificially deposited bacterial strains on stainless steel (SS) and plastic coupons. Three biological trials were conducted for each study. To determine whether the increase, decrease, or difference in the bacterial populations and other parameters like water activity (aw) was statistically significant, a T-test was performed (p ≤ 0.05 was considered significant). Results of 16S rRNA sequencing revealed the presence of certain bacterial species in PIF, such as Lactococcus lactis, B. cereus, Listeria monocytogenes, Staphylococcus aureus, Salmonella enterica, etc. distributed across a relative abundance of >25%, <25%, and ≤3%. After the enrichment and isolation as per Bacteriological Analytical Manual (BAM), C. sakazakii or S. enterica were not detected, while colonies exhibiting a blue-green appearance resembling Listeria spp. and certain Bacillus spp. were subjected to WGS for species-level identification. The assembly_1 from formulation 1 was confirmed as B. cereus sequence type 2255 and was renamed as B. cereus DFPST-SP1 in the contribution of this thesis work. The storage study conducted on PIF inoculated with C. sakazakii and B. cereus DFPST-SP1 at 33% RH showed that there was 0.25-0.27 log CFU/g reduction towards the end of 28 d, but no significant difference was observed between the two strains. The thermal challenge study revealed that the newly isolated B. cereus strain and C. sakazakii used in this study were highly thermotolerant. Based on the sanitizer challenge study, 70% ethanol was significantly more effective in reducing populations of C. sakazakii as compared to B. cereus. Moreover, higher log reductions of C. sakazakii 587 populations on stainless steel coupons compared to plastic coupons were observed, indicating that bacteria adhere more tightly to plastic surfaces than stainless steel (SS). Overall, the findings of this study shed new light on bolstering the safety standards of PIF and highlight the need for improved cleaning and sanitization procedures within manufacturing facilities in order to ensure the safety of reconstituted PIF, thereby enhancing public health, particularly infants and neonates
Integrating Deep Learning And Innovative Feature Selection For Improved Short-Term Price Prediction In Futures Markets
No full textThis study presents a novel approach for predicting short-term price movements in futures markets using advanced deep-learning models, namely LSTM, CNN_LSTM, and GRU_LSTM. By incorporating cophenetic correlation in feature preparation, the study addresses the challenges posed by sudden fluctuations and price spikes while maintaining diversification and utilizing a limited number of variables derived from daily public data. However, the effectiveness of adding features relies on appropriate feature selection, even when employing powerful deep-learning models. To overcome this limitation, an innovative feature selection method is proposed, which combines cophenetic correlation-based hierarchical linkage clustering with the XGBoost importance listing function. This method efficiently identifies and integrates the most relevant features, significantly improving price prediction accuracy. The empirical findings contribute valuable insights into price prediction accuracy and the potential integration of algorithmic and intuitive approaches in futures markets. Moreover, the developed feature preparation method enhances the performance of all deep learning models, including LSTM, CNN_LSTM, and GRU_LSTM. This study contributes to the advancement of price prediction techniques by demonstrating the potential of integrating deep learning models with innovative feature selection methods. Traders and investors can leverage this approach to enhance their decision-making processes and optimize trading strategies in dynamic and complex futures markets