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Using Spatio-Temporal Graph Neural Networks to Estimate Fleet-Wide Photovoltaic Performance Degradation Patterns
Get PDFAccurate estimation of photovoltaic (PV) system performance is crucial for determining its feasibility as a power generation technology and financial asset. PV-based energy solutions offer a viable alternative to traditional energy resources due to their superior Levelized Cost of Energy (LCOE). A significant challenge in assessing the LCOE of PV systems lies in understanding the Performance Loss Rate (PLR) for large fleets of PV systems. Estimating the PLR of PV systems becomes increasingly important in the rapidly growing PV industry. Precise PLR estimation benefits PV users by providing real-time monitoring of PV module performance, while explainable PLR estimation assists PV manufacturers in studying and enhancing the performance of their products. However, traditional PLR estimation methods based on statistical models have notable drawbacks. Firstly, they require user knowledge and decision-making. Secondly, they fail to leverage spatial coherence for fleet-level analysis. Additionally, these methods inherently assume the linearity of degradation, which is not representative of real world degradation. To overcome these challenges, we propose a novel graph deep learning-based decomposition method called the Spatio-Temporal Graph Neural Network for fleet-level PLR estimation (PV-stGNN-PLR). PV-stGNN-PLR decomposes the power timeseries data into aging and fluctuation components, utilizing the aging component to estimate PLR. PVstGNN-PLR exploits spatial and temporal coherence to derive PLR estimation for all systems in a fleet and imposes flatness and smoothness regularization in loss function to ensure the successful disentanglement between aging and fluctuation. We have evaluated PV-stGNN-PLR on three simulated PV datasets consisting of 100 inverters from 5 sites. Experimental results show that PV-stGNN-PLR obtains a reduction of 33.9% and 35.1% on average in Mean Absolute Percent Error (MAPE) and Euclidean Distance (ED) in PLR degradation pattern estimation compared to the state-of-the-art PLR estimation methods
Life and Mental Health Outcomes of Justice-Involved Youth as a result of Targeted Racial Discrimination and Criminalization
Get PDFPatients on Antithrombotic Agents with Small Bowel Bleeding –Yield of Small Bowel Capsule Endoscopy and Subsequent Management
Get PDFBackground and Aims: Small bowel gastrointestinal bleeding (GIB) is associated with multiple blood transfusions, prolonged and/or multiple hospital admissions, utilization of significant healthcare resources, and negative effects on patient quality of life. There is a well-recognized association between antithrombotic medications and small bowel GIB. We aimed to identify the diagnostic yield of small bowel capsule endoscopy (SBCE) in patients on antithrombotic medications and the impact of SBCE on treatment course. Methods: The electronic medical records of nineteen hundred eighty-six patients undergoing SBCE were retrospectively reviewed. Results: The diagnostic yield for detecting stigmata of recent bleeding and/or actively bleeding lesions in SBCE was higher in patients that were on antiplatelet agents (21.6%), patients on anticoagulation (22.5%), and in patients that had their SBCE performed while they were inpatient (21.8%), when compared to the patients not on antiplatelet agents (12.1%), patients not on anticoagulation (13.5%), and with patients that had their SBCE performed in the outpatient setting (12%). Of 318 patients who had stigmata of recent bleeding and/or actively bleeding lesion(s) identified on SBCE, SBCE findings prompted endoscopic evaluation (small bowel enteroscopy, esophagogastroduodenoscopy (EGD), and/or colonoscopy) in 25.2%, with endoscopic hemostasis attempted in 52.5%. Conclusions: Our study, the largest conducted to date, emphasizes the importance of performing SBCE as part of the evaluation for suspected small bowel bleeding, particularly in patients taking antithrombotic therapy, and especially during their inpatient hospital stay
TriSC: Low-Cost Design of Trigonometric Functions with Quasi Stochastic Computing
Get PDFLow-cost and hardware-efficient design of trigonometric functions is challenging. Stochastic computing (SC), an emerging computing model processing random bit-streams, offers promising solutions for this problem. The existing implementations, however, often overlook the importance of the data converters necessary to generate the needed bit-streams. While recent advancements in SC bit-stream generators focus on basic arithmetic operations such as multiplication and addition, energy-efficient SC design of non-linear functions demands attention to both the computation circuit and the bit-stream generator. This work introduces TriSC, a novel approach for SC-based design of trigonometric functions enjoying state-of-the-art (SOTA) quasi-random bit-streams. Unlike SOTA SC designs of trigonometric functions that heavily rely on delay elements to decorrelate bit-streams, our approach avoids delay elements while improving the accuracy of the results. TriSC yields significant energy savings of up to 92% compared to SOTA. As two novel use cases studied for the first time in SC literature, we employ the proposed design for 2D image transformation and forward kinematics of a robotic arm, two computation-intensive applications demanding low-cost trigonometric designs
Impact of a Peer-Educator Led LifeSkills Training Tobacco Module on Tobacco-Related Attitudes, Knowledge, and Susceptibility in 5th and 6th Graders
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The Transformation of Social Work in Ukraine Before and During the War
Get PDFBackground: This article aims to review the development of the social work profession in Ukraine and to describe the impact of social, economic and political changes on social work practices and education. Methods: A comprehensive literature review and participant observation methods informed this study. A case study of a Polish community’s response to Ukrainian war refugees illustrates how social workers might capitalize on current social structures to continue strengthening civil society in Ukraine. Findings and Discussion: Social Work, focusing on the fit between person and environment, is shaped by knowledge, culture and belief systems. Ukraine’s history and transition from communist/centralized thinking to civil society is reflected in the development of social work to date. The impact of Russian invasions has hindered and strengthened how social workers can recognize and respond to needs. Limitations include the time lag between published articles and the rapidly changing situation in Ukraine. Originality/Value: Few articles focus on social work development in Ukraine, which adds to this article’s originality and relevance
Uncertainty Quantification in Machine Learning Models Via Gaussian Process Regression: A Comparative Study
Get PDFAs the use of Machine learning models in science and engineering continues to increase, there is an increasing need for quantifying the uncertainties inherent in the predictions of these models. The more complex a model is, the more the uncertainties in its predictions increase. Amongst the plethora of methodologies used in quantifying uncertainties lies Gaussian Process Regression (GPR). GPR surmounts some of the popular shortfalls of other state-of-the-art methodologies. Although GPR has some quick wins in its application for uncertainty quantification, it is plagued with some shortfalls, such as scalability issues when the feature space increases as well as an increase in computational time. Our current study compares the computational time besides quantifying the uncertainties in the predictions from the machine learning models across different covariance structures. Specifically, we used 2D diffraction patterns recorded on a 2D area detector using high-energy X-ray diffraction (HEXRD) to predict the volume fraction of the β-phase of Ti-6Al-4v (Ti64). To achieve this, we reduced the features through Principal Component Analysis and used components that account for 95%, 99% and 99.5% variation in the 2D diffraction images for each of the four datasets used respectively. With the current methodology, we have scaled the application of GPR to high-dimensional cases while we are exploring other methodologies that will reduce the computational time when the sample size becomes large. The goal of the project is to integrate these methodologies to achieve scalability with shorter computational time
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