Browsing by Author "Engineering Technology, Purdue School of Engineering and Technology"

Now showing 1 - 10 of 12
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    Annotation and Information Extraction of Consumer-Friendly Health Articles for Enhancing Laboratory Test Reporting
    (American Medical Informatics Association, 2024-01-11) He, Zhe; Tian, Shubo; Erdengasileng, Arslan; Hanna, Karim; Gong, Yang; Zhang, Zhan; Luo, Xiao; Lustria, Mia Liza A.; Engineering Technology, Purdue School of Engineering and Technology
    Viewing laboratory test results is patients' most frequent activity when accessing patient portals, but lab results can be very confusing for patients. Previous research has explored various ways to present lab results, but few have attempted to provide tailored information support based on individual patient's medical context. In this study, we collected and annotated interpretations of textual lab result in 251 health articles about laboratory tests from AHealthyMe.com. Then we evaluated transformer-based language models including BioBERT, ClinicalBERT, RoBERTa, and PubMedBERT for recognizing key terms and their types. Using BioPortal's term search API, we mapped the annotated terms to concepts in major controlled terminologies. Results showed that PubMedBERT achieved the best F1 on both strict and lenient matching criteria. SNOMED CT had the best coverage of the terms, followed by LOINC and ICD-10-CM. This work lays the foundation for enhancing the presentation of lab results in patient portals by providing patients with contextualized interpretations of their lab results and individualized question prompts that they can, in turn, refer to during physician consults.
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    DAG-based Task Orchestration for Edge Computing
    (IEEE, 2022) Li, Xiang; Abdallah, Mustafa; Suryavansh, Shikhar; Chiang, Mung; Bagchi, Saurabh; Engineering Technology, Purdue School of Engineering and Technology
    Edge computing promises to exploit underlying computation resources closer to users to help run latency-sensitive applications such as augmented reality and video analytics. However, one key missing piece has been how to incorporate personally owned, unmanaged devices into a usable edge computing system. The primary challenges arise due to the heterogeneity, lack of interference management, and unpredictable availability of such devices. In this paper we propose an orchestration framework IBDASH, which orchestrates application tasks on an edge system that comprises a mix of commercial and personal edge devices. IBDASH targets reducing both end-to-end latency of execution and probability of failure for applications that have dependency among tasks, captured by directed acyclic graphs (DAGs). IBDASH takes memory constraints of each edge device and network bandwidth into consideration. To assess the effectiveness of IBDASH, we run real application tasks on real edge devices with widely varying capabilities. We feed these measurements into a simulator that runs IBDASH at scale. Compared to three state-of-the-art edge orchestration schemes and two intuitive baselines, IBDASH reduces the end-to-end latency and probability of failure, by 14% and 41% on average respectively. The main takeaway from our work is that it is feasible to combine personal and commercial devices into a usable edge computing platform, one that delivers low and predictable latency and high availability.
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    Economic Operation of Utility-Connected Microgrids in a Fast and Flexible Framework Considering Non-Dispatchable Energy Sources
    (MDPI, 2022) Akbari, Rasoul; Tajalli, Seyede Zahra; Kavousi-Fard, Abdollah; Izadian, Afshin; Engineering Technology, Purdue School of Engineering and Technology
    This paper introduces a modified consensus-based real-time optimization framework for utility-connected and islanded microgrids scheduling in normal conditions and under cyberattacks. The exchange of power with the utility is modeled, and the operation of the microgrid energy resources is optimized to minimize the total energy cost. This framework tracks both generation and load variations to decide optimal power generations and the exchange of power with the utility. A linear cost function is defined for the utility where the rates are updated at every time interval. In addition, a realistic approach is taken to limit the power generation from renewable energy sources, including photovoltaics (PVs), wind turbines (WTs), and dispatchable distributed generators (DDGs). The maximum output power of DDGs is limited to their ramp rates. Besides this, a specific cloud-fog architecture is suggested to make the real-time operation and monitoring of the proposed method feasible for utility-connected and islanded microgrids. The cloud-fog-based framework is flexible in applying demand response (DR) programs for more efficiency of the power operation. The algorithm’s performance is examined on the 14 bus IEEE network and is compared with optimal results. Three operating scenarios are considered to model the load as light and heavy, and after denial of service (DoS) attack to indicate the algorithm’s feasibility, robustness, and proficiency. In addition, the uncertainty of the system is analyzed using the unscented transformation (UT) method. The simulation results demonstrate a robust, rapid converging rate and the capability to track the load variations due to the probable responsive loads (considering DR programs) or natural alters of load demand.
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    Hybrid energy storage characterization for power profile enhancement in split-shaft wind energy conversion systems
    (Wiley, 2022) Akbari, Rasoul; Izadian, Afshin; Engineering Technology, Purdue School of Engineering and Technology
    This paper characterizes an integrated hybrid energy storage unit required to support the new generator excitation system developed for doubly-fed induction generators in the split-shaft wind energy conversion units. The goal is to improve the power quality while significantly reducing the generator excitation power rating and component counts. The rotor excitation circuit is modified to directly include the storage to its DC link. The output power fluctuation can be attenuated solely by utilizing the rotor-side converter making it self-sufficient from the grid connection. The storage characteristics are identified based on several system design parameters, including the system inertia, inverter capacity, and energy storage capacity. The obtained power generation characteristics suggest an energy storage mix of fast-acting types with a high energy capacity and moderate acting time. A feedback controller is designed to maintain the charge in the storage within the required limits. Adaptive model-predictive control is also developed to reduce power generation fluctuations. The proposed system is investigated and simulated in MATLAB Simulink at different wind speeds to validate the results and demonstrate the system's dynamic performance. It is demonstrated that the system's inertia is critical to damping the high-frequency oscillations of the wind power fluctuations. It is also shown that the bandwidth of the control system is determined by the system inertia and the size of the storage and inverter power rating.
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    Morshed: Guiding Behavioral Decision-Makers towards Better Security Investment in Interdependent Systems
    (Association for Computing Machinery, 2021) Abdallah, Mustafa; Woods, Daniel; Naghizadeh, Parinaz; Khalil, Issa; Cason, Timothy; Sundaram, Shreyas; Bagchi, Saurabh; Engineering Technology, Purdue School of Engineering and Technology
    We model the behavioral biases of human decision-making in securing interdependent systems and show that such behavioral decision-making leads to a suboptimal pattern of resource allocation compared to non-behavioral (rational) decision-making. We provide empirical evidence for the existence of such behavioral bias model through a controlled subject study with 145 participants. We then propose three learning techniques for enhancing decision-making in multi-round setups. We illustrate the benefits of our decision-making model through multiple interdependent real-world systems and quantify the level of gain compared to the case in which the defenders are behavioral. We also show the benefit of our learning techniques against different attack models. We identify the effects of different system parameters (e.g., the defenders' security budget availability and distribution, the degree of interdependency among defenders, and collaborative defense strategies) on the degree of suboptimality of security outcomes due to behavioral decision-making.
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    Multi-reference global registration of individual A-lines in adaptive optics optical coherence tomography retinal images
    (Elsevier, 2021) Kurokawa, Kazuhiro; Crowell, James A.; Do, Nhan; Lee, John J.; Miller, Donald T.; Engineering Technology, Purdue School of Engineering and Technology
    Significance: Adaptive optics optical coherence tomography (AO-OCT) technology enables non-invasive, high-resolution three-dimensional (3D) imaging of the retina and promises earlier detection of ocular disease. However, AO-OCT data are corrupted by eye-movement artifacts that must be removed in post-processing, a process rendered time-consuming by the immense quantity of data. Aim: To efficiently remove eye-movement artifacts at the level of individual A-lines, including those present in any individual reference volume. Approach: We developed a registration method that cascades (1) a 3D B-scan registration algorithm with (2) a global A-line registration algorithm for correcting torsional eye movements and image scaling and generating global motion-free coordinates. The first algorithm corrects 3D translational eye movements to a single reference volume, accelerated using parallel computing. The second algorithm combines outputs of multiple runs of the first algorithm using different reference volumes followed by an affine transformation, permitting registration of all images to a global coordinate system at the level of individual A-lines. Results: The 3D B-scan algorithm estimates and corrects 3D translational motions with high registration accuracy and robustness, even for volumes containing microsaccades. Averaging registered volumes improves our image quality metrics up to 22 dB. Implementation in CUDA™ on a graphics processing unit registers a 512 × 512 × 512 volume in only 10.6 s, 150 times faster than MATLAB™ on a central processing unit. The global A-line algorithm minimizes image distortion, improves regularity of the cone photoreceptor mosaic, and supports enhanced visualization of low-contrast retinal cellular features. Averaging registered volumes improves our image quality up to 9.4 dB. It also permits extending the imaging field of view (∼2.1 × ) and depth of focus (∼5.6 × ) beyond what is attainable with single-reference registration. Conclusions: We can efficiently correct eye motion in all 3D at the level of individual A-lines using a global coordinate system.
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    Multi-reference global registration of individual A-lines in adaptive optics optical coherence tomography retinal images (Publisher’s Note)
    (SPIE, 2021) Kurokawa, Kazuhiro; Crowell, James A.; Do, Nhan; Lee, John J.; Miller, Donald T.; Engineering Technology, Purdue School of Engineering and Technology
    The note corrects an error that appeared in Equation 14 of the originally published article.
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    Pain and Nausea Intensity, Social Function, and Psychological Well-Being among Women with Metastatic Breast Cancer
    (Sage, 2022-11-01) Senkpeil, Ryan R.; Olson, Julie S.; Fortune, Erica E.; Zaleta, Alexandra K.; Engineering Technology, Purdue School of Engineering and Technology
    Advances in diagnostics and therapeutics have improved prognosis for metastatic breast cancer (MBC). Yet, treatment and disease burden-including experiences of pain and nausea-present practical and emotional challenges. To better support patients and enhance quality of life, deeper understanding of the pathways linking physical and psychological health is needed. To this end, we examined associations of pain and nausea with depression and anxiety among women with MBC. In doing so, we highlighted social function as a potentially important mechanism in this relationship. This observational, cross-sectional study included 148 predominantly non-Hispanic White, highly educated women living with MBC. Multivariate regression models demonstrated that more intense pain and nausea were significantly associated with higher levels of depression and anxiety (p < .001). Causal mediation analyses confirmed significant indirect effects whereby decreases in social function associated with pain and nausea contributed to depression and anxiety. Thus, our findings illustrate decreased social function as one pathway through which pain and nausea contribute to escalation of depression and anxiety. Our results, therefore, underscore the importance of supporting social function among women with MBC to potentially reduce psychological sequelae of pain and nausea.
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    Perception In Leading Change: The Role Of Academic Leaders as Change Agents
    (Virginia Tech, 2022) Finn, Edward W., III; Feldhaus, Charles; Engineering Technology, Purdue School of Engineering and Technology
    Too often organizational change is seen as a negative force. This perception of the specific change as bad causes tremendous disruption and misunderstanding among faculty and academic leaders. This study contends that the more relevant issue is whether academic leaders communicate the vision and strategy for change effectively. Furthermore, the crux of the matter is not whether high-quality, rich communication exists but depends more heavily on the perception of faculty undergoing change. In this article, the authors will compare nine faculty members’ responses to a perceptual survey dealing with organizational change, interviews with questions created using the survey as a basis, and archival data showing the availability and opportunity for involvement in the change process. This comparison will allow similarities and discrepancies to be examined between faculty perceptions of leaders, while also taking the institutional context into account.
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    Repurposing MALDI-TOF MS for effective antibiotic resistance screening in Staphylococcus epidermidis using machine learning
    (Springer Nature, 2024-10-15) Ren, Michael; Chen, Qiang; Zhang, Jing; Engineering Technology, Purdue School of Engineering and Technology
    The emergence of Staphylococcus epidermidis as a significant nosocomial pathogen necessitates advancements in more efficient antimicrobial resistance profiling. However, existing culture-based and PCR-based antimicrobial susceptibility testing methods are far too slow or costly. This study combines machine learning with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) to develop predictive models for various antibiotics using a comprehensive dataset containing thousands of S. epidermidis isolates. Optimized machine learning models utilized feature selection and achieved high AUROC scores ranging from 0.80 to 0.95 while maintaining AUPRC scores up to 0.97. Shapley Additive exPlanations were employed to analyze relevant features and assess the significance of corresponding protein biomarkers while also verifying that predictive power was derived from the detection of proteins rather than noise. Antimicrobial resistance models were validated externally to evaluate model performance outside the original data collection site. The approaches and findings in this study demonstrate a significant advancement in rapid, cost-effective antimicrobial resistance profiling, offering a promising solution for improving treatments for nosocomial infections and being potentially applicable to other microbial pathogens in the future.