Modern design of nuclear facilities represents unique challenges: enabling the design of complex advanced concepts, supporting geographically dispersed teams, and supporting first-of-a-kind system development. Errors ...
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Olive activated biochar (OAB) was prepared from waste de-oiled olive pomace (sansa esausta, SE) through carbonization followed by combined KOH and thermal activation. The activation process was optimized using central...
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Proton beam therapy is an advanced form of cancer radiotherapy that uses high-energy proton beams to deliver precise and targeted radiation to tumors. This helps to mit-igate unnecessary radiation exposure in healthy ...
Proton beam therapy is an advanced form of cancer radiotherapy that uses high-energy proton beams to deliver precise and targeted radiation to tumors. This helps to mit-igate unnecessary radiation exposure in healthy tissues. Real-time imaging of prompt gamma rays with Compton cameras has been suggested to improve therapy efficacy. However, the camera's non-zero time resolution leads to incorrect interaction classifications and noisy images that are insufficient for accurately assessing proton delivery in patients. To address the challenges posed by the Compton camera's image quality, machine learning techniques are employed to classify and refine the generated data. These machine-learning techniques include recurrent and feedforward neural networks. A PyTorch model was designed to improve the data captured by the Compton camera. This decision was driven by PyTorch's flexibility, powerful capabilities in handling sequential data, and enhanced G PU usage. This accelerates the model's computations on large-scale radiotherapy data. Through hyperparameter tuning, the validation accuracy of our PyTorch model has been improved from an initial 7 % to over 60 %. Moreover, the PyTorch Distributed Data Parallelism strategy was used to train the RNN models on multiple G PU s, which significantly reduced the training time with a minor impact on model accuracy.
Cancer is still one of the most devastating diseases of our time. One way of automatically classifying tumor samples is by analyzing its derived molecular information (i.e., its genes expression signatures). In this w...
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Magnetic Particle Imaging (MPI) is a novel technique developed for remotely detecting magnetic nanoparticle (MNP) tracers, with great potential for biomedical imaging (as an alternative to traditional methods like MRI...
Magnetic Particle Imaging (MPI) is a novel technique developed for remotely detecting magnetic nanoparticle (MNP) tracers, with great potential for biomedical imaging (as an alternative to traditional methods like MRI or CT), cell tracking, targeted drug delivery and magnetic hyperthermia. More recently, MPI has been studied as a potential method of non-contact temperature measurement. This work presents a simulation study of the multi-color MPI method tailored for 3D temperature imaging, discusses the feasibility of the method for 3D temperature measurements, and shows a parallel implementation of the multi-color T-MPI reconstruction algorithm in graphics processing unit (GPU). While the use of the parallel algorithm resulted in executions about 40x faster when compared to the serial implementation, the method exhibited serious limitations in accurately resolving particle temperatures between the calibration temperatures by interpolation.
IT Governance are one of the needs in managing Enterprise Level IT. This study shows part of the decision domain of IT Governance Help, which are IT Investment and Prioritization. The purpose of this study is to deter...
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While various models and computational tools have been proposed for structure and property analysis of molecules, generating molecules that conform to all desired structures and properties remains a challenge. Here, w...
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Nowadays, The roads have increased the number of streetlights for the roads vehicles/pedestrians, which raises investment and energy. Observations made to obtain most of the road lights are always active at night, eve...
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ISBN:
(数字)9780738125176
ISBN:
(纸本)9781665418614
Nowadays, The roads have increased the number of streetlights for the roads vehicles/pedestrians, which raises investment and energy. Observations made to obtain most of the road lights are always active at night, even when there are no vehicles or pedestrians on the road. The problems that occur are the waste of energy sources that are used in streetlights. This research designs the concept of intelligent traffic flow based (LED) for energy optimization, maximum efficiency. This concept uses intelligent light architecture using the LoRaWAN Mesh network. The application of this concept offers system reliability, reduces costs, and makes user satisfaction. The results of this study are demonstrated by experimenting with comparing conventional LED lights. The proposed system, resulting in 33% to 62% energy savings depending on when the usage process in streetlights. Smart lighting LEDs with LoRaWAN provide a remote-control mechanism that can be dynamically adjusted based on environmental conditions, distance, and automatic motion.
Real-time, guaranteed safe trajectory planning is vital for navigation in unknown environments. However, real-time navigation algorithms typically sacrifice robustness for computation speed. Alternatively, provably sa...
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