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Item 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 TechnologyThis 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.Item Review—Combining Experimental and Engineering Aspects of Catalyst Design for Photoelectrochemical Water Splitting(IOP, 2022) Sharma, Chhavi; D., Pooja; Thakur, Anupma; Negi, Y. S.; Engineering Technology, Purdue School of Engineering and TechnologyHydrogen is one of the cleanest, most favourable, and most practical energy transferors. However, its efficient generation, storage and transportation are still a challenge. There are various routes available toward greener hydrogen. Solar-driven splitting is considered a cleaner method with no harmful emission and viability of up-scaling. Various semiconductors were studied for photo-electrochemical catalysis to improve overall efficiency of the system (i.e. Solar-to-Hydrogen (STH)). The insistence of framing this article is to offer an intense evaluation of scientific and technical aspects of available designing strategies' for photocatalysts and recent fruitful advancements towards product development. This review might act as a handbook for budding researchers and provide a cutting-edge towards innovative & efficient catalyst designing strategy to improve efficiency for working scientists.Item 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 TechnologyWe 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.Item 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 TechnologySignificance: 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.Item 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 TechnologyAdvances 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.Item 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 TechnologyViewing 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.Item Electrocatalytic CO2 reduction on earth abundant 2D Mo2C and Ti3C2 MXenes(Royal Society of Chemistry, 2020) Attanayake, Nuwan H.; Banjade, Huta R.; Thenuwara, Akila C.; Anasori, Babak; Yan, Qimin; Strongin, Daniel R.; Engineering Technology, School of Engineering and TechnologyMo2C and Ti3C2 MXenes were investigated as earth-abundant electrocatalyts for the CO2 reduction reaction (CO2RR). Mo2C and Ti3C2 exhibited faradaic efficiencies of 90% (250 mV overpotential) and 65% (650 mV overpotential), respectively, for the reduction of CO2 to CO in acetonitrile using an ionic liquid electrolyte. The use of ionic liquid 1-ethyl-2-methylimidazolium tetrafluoroborate as an electrolyte in organic solvent suppressed the competing hydrogen evolution reaction. Density functional theory (DFT) calculations suggested that the catalytic active sites are oxygen vacancy sites on both MXene surfaces. Also, a spontaneous dissociation of adsorbed COOH species to a water molecule and adsorbed CO on Mo2C promote the CO2RR.Item Determination of Internal Elevation Fluctuation from CCTV Footage of Sanitary Sewers Using Deep Learning(MDPI, 2021) Ji, Hyon Wook; Yoo, Sung Soo; Koo, Dan Daehyun; Kang, Jeong-Hee; Engineering Technology, School of Engineering and TechnologyThe slope of sewer pipes is a major factor for transporting sewage at designed flow rates. However, the gradient inside the sewer pipe changes locally for various reasons after construction. This causes flow disturbances requiring investigation and appropriate maintenance. This study extracted the internal elevation fluctuation from closed-circuit television investigation footage, which is required for sanitary sewers. The principle that a change in water level in sewer pipes indirectly indicates a change in elevation was applied. The sewage area was detected using a convolutional neural network, a type of deep learning technique, and the water level was calculated using the geometric principles of circles and proportions. The training accuracy was 98%, and the water level accuracy compared to random sampling was 90.4%. Lateral connections, joints, and outliers were removed, and a smoothing method was applied to reduce data fluctuations. Because the target sewer pipes are 2.5 m concrete reinforced pipes, the joint elevation was determined every 2.5 m so that the internal slope of the sewer pipe would consist of 2.5 m linear slopes. The investigative method proposed in this study is effective with high economic feasibility and sufficient accuracy compared to the existing sensor-based methods of internal gradient investigation.Item Environment-independent In-baggage Object Identification Using WiFi Signals(IEEE Xplore, 2021-10) Shi, Cong; Zhao, Tianming; Xie, Yucheng; Zhang, Tianfang; Wang, Yan; Guo, Xiaonan; Chen, Yingying; Engineering Technology, School of Engineering and TechnologyLow-cost in-baggage object identification is highly demanded in enhancing public safety and smart manufacturing. Existing approaches usually require specialized equipment and heavy deployment overhead, making them hard to scale for wide deployment. The recent WiFi-based approach is unsuitable for practical deployment as it did not address dynamic environmental impacts. In this work, we propose an environment-independent in-baggage object identification system by leveraging low-cost WiFi. We exploit the channel state information (CSI) to capture material and shape characteristics to facilitate fine-grained inbaggage object identification. A major challenge of building such a system is that CSI measurements are sensitive to real-world dynamics, such as different types of baggage, time-varying ambient noises and interferences, and different deployment environments. To tackle these problems, we develop WiFi features based on polarized directional antennas that can capture objects’ material and shape characteristics. A convolutional neural network-based model is developed to constructively integrate the WiFi features and perform accurate in-baggage object identification. We also develop a material-based domain adaptation using adversarial learning to facilitate fast deployments in different environments. We conduct extensive experiments involving 14 representation objects, 4 types of bags in 3 different room environments. The results show that our system can achieve over 97% in the same environment, and our domain adaptation method can improve the object identification accuracy by 42% when the system is deployed in a new environment with little training.Item Building capacity for socio-ecological change through the campus farm: A mixed-methods study(Taylor & Francis, 2022) Williamson, Francesca A.; Rollings, Amber J.; Fore, Grant A.; Angstmann, Julia L.; Sorge, Brandon H.; Engineering Technology, School of Engineering and TechnologyGiven the ongoing socio-ecological crises, higher education institutions need curricular interventions to support students in developing the knowledge, skills, and perspectives needed to create a sustainable future. Campus farms are increasingly becoming sites for sustainability and environmental education toward this end. This paper describes the design and outcomes of a farm-situated place-based experiential learning (PBEL) intervention in two undergraduate biology courses and one environmental studies course over two academic years. We conducted a mixed-method study using pre/post-surveys and focus groups to examine the relationship between the PBEL intervention and students’ sense of place and expressions of pro-environmentalism. The quantitative analysis indicated measurable shifts in students’ place attachment and place-meaning scores. The qualitative findings illustrate a complex relationship between students’ academic/career interests, backgrounds, and pro-environmentalism. We integrated these findings to generate a model of sustainability learning through PBEL and argue for deepening learning to encourage active participation in socio-ecological change.