Browsing by Author "Dhakal, Rabin (TTU)"
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Item A Novel Hybrid Method for Short-Term Wind Speed Prediction Based on Wind Probability Distribution Function and Machine Learning Models(2022) Dhakal, Rabin (TTU); Sedai, Ashish (TTU); Pol, Suhas (TTU); Parameswaran, Siva (TTU); Nejat, Ali (TTU); Moussa, Hanna (TTU)The need to deliver accurate predictions of renewable energy generation has long been recognized by stakeholders in the field and has propelled recent improvements in more precise wind speed prediction (WSP) methods. Models such as Weibull-probability-density-based WSP (WEB), Rayleigh-probability-density-based WSP (RYM), autoregressive integrated moving average (ARIMA), Kalman filter and support vector machines (SVR), artificial neural network (ANN), and hybrid models have been used for accurate prediction of wind speed with various forecast horizons. This study intends to incorporate all these methods to achieve a higher WSP accuracy as, thus far, hybrid wind speed predictions are mainly made by using multivariate time series data. To do so, an error correction algorithm for the probability-density-based wind speed prediction model is introduced. Moreover, a comparative analysis of the performance of each method for accurately predicting wind speed for each time step of short-term forecast horizons is performed. All the models studied are used to form the prediction model by optimizing the weight function for each time step of a forecast horizon for each model that contributed to forming the proposed hybrid prediction model. The National Oceanic and Atmospheric Administration (NOAA) and System Advisory Module (SAM) databases were used to demonstrate the accuracy of the proposed models and conduct a comparative analysis. The results of the study show the significant improvement on the performance of wind speed prediction models through the development of a proposed hybrid prediction model.Item Automated Detection and Scoring of Tumor-Infiltrating Lymphocytes in Breast Cancer Histopathology Slides(2023) Yosofvand, Mohammad (TTU); Khan, Sonia Y. (TTUHSC); Dhakal, Rabin (TTU); Nejat, Ali (TTU); Moustaid-Moussa, Naima (TTU); Rahman, Rakhshanda Layeequr (TTUHSC); Moussa, Hanna (TTU)Detection of tumor-infiltrating lymphocytes (TILs) in cancer images has gained significant importance as these lymphocytes can be used as a biomarker in cancer detection and treatment procedures. Our goal was to develop and apply a TILs detection tool that utilizes deep learning models, following two sequential steps. First, based on the guidelines from the International Immuno-Oncology Biomarker Working Group (IIOBWG) on Breast Cancer, we labeled 63 large pathology imaging slides and annotated the TILs in the stroma area to create the dataset required for model development. In the second step, various machine learning models were employed and trained to detect the stroma where U-Net deep learning structure was able to achieve 98% accuracy. After detecting the stroma area, a Mask R-CNN model was employed for the TILs detection task. The R-CNN model detected the TILs in various images and was used as the backbone analysis network for the GUI development of the TILs detection tool. This is the first study to combine two deep learning models for TILs detection at the cellular level in breast tumor histopathology slides. Our novel approach can be applied to scoring TILs in large cancer slides. Statistical analysis showed that the output of the implemented approach had 95% concordance with the scores assigned by the pathologists, with a p-value of 0.045 (n = 63). This demonstrated that the results from the developed software were statistically meaningful and highly accurate. The implemented approach in analyzing whole tumor histology slides and the newly developed TILs detection tool can be used for research purposes in biomedical and pathology applications and it can provide researchers and clinicians with the TIL score for various input images. Future research using additional breast cancer slides from various sources for further training and validation of the developed models is necessary for more inclusive, rigorous, and robust clinical applications.Item CFD analysis of gravity-fed drag-type in-pipe water turbine to determine the optimal deflector-to-turbine position(2023) Gautam, Shishir; Sedai, Ashish (TTU); Dhakal, Rabin (TTU); Sedhai, Bijaya Kumar; Pol, Suhas (TTU)In-pipe hydroelectric power generation is a relatively new clean energy power generation technology. This new clean energy technology has been identified to be feasible after successful commercial installation in different parts of the world. Several researchers worldwide have studied the optimal turbine type, the optimal number of blades in turbine, introduction of suitable deflector, etc. for this technology. However, the effect of the turbine's position relative to the upstream deflector on its performance has not been studied so far. This research encompasses a numerical study of the in-pipe hydroelectric power generation turbine to identify the optimal position of the turbine from the deflector. The study was performed for a 160-mm diameter pipeline and a 126-mm turbine height and aims to predict the behavior of larger diameter pipelines for commercial installation. The numerical study was performed for a hollow-type drag turbine at 6 different rotational speeds and 10 different turbine positions. The results suggest that the performance characteristics of drag-type turbine are erratic, thus leaving little space to draw a firm conclusion about the turbine's performance. However, there was an increase in pressure difference, head and available theoretical power with the increase in rotational speed for all the positions. It was also found that such turbines were generally more efficient at slightly higher rotational speeds, i.e. speed greater than 40 rad/s, and at about the distance of 0.65D (where D is the pipe diameter) between deflector's eye and turbine.Highlights: •Drag-type water turbines can significantly contribute to the production of clean energy. •We varied the turbine position and rotational speed to see how these parameters affects the turbine performance. •Computational fluid dynamics approach is used to study the behavior of turbine at different operating conditions.Item Development and Application of MAGIC-f Gel in Cancer Research and Medical Imaging(2021) Dhakal, Rabin (TTU); Yosofvand, Mohammad (TTU); Moussa, Hanna (TTU)Much of the complex medical physics work requires radiation dose delivery, which requires dosimeters to accurately measure complex three-dimensional dose distribution with good spatial resolution. MAGIC-f polymer gel is one of the emerging new dosimeters widely used in medical physics research. The purpose of this study was to present an overview of polymer gel dosimetry, using MAGIC-f gel, including its composition, manufacture, imaging, calibration, and application to medical physics research. In this review, the history of polymer gel development is presented, along with the applications so far. Moreover, the most important experiments/applications of MAGIC-f polymer gel are discussed to illustrate the behavior of gel on different conditions of irradiation, imaging, and manufacturing techniques. Finally, various future works are suggested based on the past and present works on MAGIC-f gel and polymer gel in general, with the hope that these bits of knowledge can provide important clues for future research on MAGIC-f gel as a dosimeter.Item Review of Biological Effects of Acute and Chronic Radiation Exposure on Caenorhabditis elegans(2021) Dhakal, Rabin (TTU); Yosofvand, Mohammad (TTU); Yavari, Mahsa (TTU); Abdulrahman, Ramzi; Schurr, Ryan; Moustaid-Moussa, Naima (TTU); Moussa, Hanna (TTU)Knowledge regarding complex radiation responses in biological systems can be enhanced using genetically amenable model organisms. In this manuscript, we reviewed the use of the nematode, Caenorhabditis elegans (C. elegans), as a model organism to investigate radiation’s biological effects. Diverse types of experiments were conducted on C. elegans, using acute and chronic exposure to different ionizing radiation types, and to assess various biological responses. These responses differed based on the type and dose of radiation and the chemical substances in which the worms were grown or maintained. A few studies compared responses to various radiation types and doses as well as other environmental exposures. Therefore, this paper focused on the effect of irradiation on C. elegans, based on the intensity of the radiation dose and the length of exposure and ways to decrease the effects of ionizing radiation. Moreover, we discussed several studies showing that dietary components such as vitamin A, polyunsaturated fatty acids, and polyphenol-rich food source may promote the resistance of C. elegans to ionizing radiation and increase their life span after irradiation.Item Wind energy as a source of green hydrogen production in the USA(2023) Sedai, Ashish (TTU); Dhakal, Rabin (TTU); Gautam, Shishir; Kumar Sedhain, Bijaya; Singh Thapa, Biraj; Moussa, Hanna (TTU); Pol, Suhas (TTU)The study incorporates an overview of the green hydrogen-production potential from wind energy in the USA, its application in power generation and the scope of substituting grey and blue hydrogen for industrial usage. Over 10 million metric tons of grey and blue hydrogen is produced in the USA annually to fulfil the industrial demand, whereas, for 1 million metric tons of hydrogen generated, 13 million metric tons of CO2 are released into the atmosphere. The research aims to provide a state-of-the-art review of the green hydrogen technology value chain and a case study on the production of green hydrogen from an 8-MW wind turbine installed in the southern plain region of Texas. This research estimates that the wind-farm capacity of 130 gigawatt-hours is required to substitute grey and blue hydrogen for fulfilling the current US annual industrial hydrogen demand of 10 million metric tons. The study investigates hydrogen-storage methods and the scope of green hydrogen-based storage facilities for energy produced from a wind turbine. This research focuses on the USA's potential to meet all its industrial and other hydrogen application requirements through green hydrogen.