The commercial future of organic photovoltaics (OPVs) relies on their efficiency and reliability. Recent progress in the field has ensured the achievement of device efficiency comparable to those of commercial silicon...
Skin pathologies encompass a spectrum of conditions, with malignancies such as melanoma representing a critical diagnostic urgency. This investigation delineates the deployment of Convolutional Neural Networks (CNNs) ...
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Computed tomography is a widely used imaging modality with applications ranging from medical imaging to material analysis. One major challenge arises from the lack of scanning information at certain angles, leading to...
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Investment is a lifetime work. Investing in stocks is a popular measure. However, investing in stocks is not easy, and losing money is not uncommon. We investigate if there is a systematic way to find buyable stocks a...
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A customer service chatbot enhanced with conversational language understanding and knowledge base is developed. Here, we explore LUIS and QnA Maker which are unified as Azure cognitive service for language. LUIS is a ...
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This article presents a method for indirectly measuring the moisture content of paddy using a cylindrical capacitive sensor combined with Charge Integrator Circuits. The moisture measurement device operates based on t...
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ISBN:
(数字)9798331543952
ISBN:
(纸本)9798331543969
This article presents a method for indirectly measuring the moisture content of paddy using a cylindrical capacitive sensor combined with Charge Integrator Circuits. The moisture measurement device operates based on the principle of the dielectric constant of paddy, which changes according to the moisture content within them. The system consists of a moisture detection section utilizing a cylindrical capacitor, a signal generator operating at a frequency of 500 Hz with a microcontroller, a signal conditioning circuit converting the signal to voltage, and a processing unit. Testing was conducted with paddy at five different moisture levels, demonstrating the ability to accurately determine the moisture content of the rice. When compared to the commercially available PM-450TH moisture meter, the results showed a linear response across the measurement range, with an R 2 value of 0.9824 and an average error of 2.6%. This technology has the potential for further development and application to other types of seeds in the future.
Making inference with spatial extremal dependence models can be computationally burdensome since they involve intractable and/or censored likelihoods. Building on recent advances in likelihood-free inference with neur...
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Making inference with spatial extremal dependence models can be computationally burdensome since they involve intractable and/or censored likelihoods. Building on recent advances in likelihood-free inference with neural Bayes estimators, that is, neural networks that approximate Bayes estimators, we develop highly efficient estimators for censored peaks-over-threshold models that use augmented data to encode censoring information in the neural network input. Our new method provides a paradigm shift that challenges traditional censored likelihood-based inference methods for spatial extremal dependence models. Our simulation studies highlight significant gains in both computational and statistical efficiency, relative to competing likelihood-based approaches, when applying our novel estimators to make inference with popular extremal dependence models, such as max-stable, r-Pareto, and random scale mixture process models. We also illustrate that it is possible to train a single neural Bayes estimator for a general censoring level, precluding the need to retrain the network when the censoring level is changed. We illustrate the efficacy of our estimators by making fast inference on hundreds-of-thousands of high-dimensional spatial extremal dependence models to assess extreme particulate matter 2.5 microns or less in diameter (PM2:5) concentration over the whole of Saudi Arabia.
Protein structure prediction in three dimensions represents a fundamental challenge in Structural Bioinformatics. Leveraging problem-specific information such as fragment insertion, secondary structure, and contact ma...
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ISBN:
(数字)9798350356632
ISBN:
(纸本)9798350356649
Protein structure prediction in three dimensions represents a fundamental challenge in Structural Bioinformatics. Leveraging problem-specific information such as fragment insertion, secondary structure, and contact maps can significantly enhance the exploration of the search space. In this study, an evolutionary algorithm is introduced, which incorporates such problem information for protein structure prediction. The proposed method employs a dynamic speciation technique alongside fragment insertion to foster population diversity. To ensure a rich variety of fragments, a fragment library is constructed using the Rosetta Quota protocol. Additionally, information from contact maps and secondary structure is integrated into two selection strategies to facilitate a more thorough exploration of the conformational search space. The results of an experimental evaluation involving 9 proteins are presented, demonstrating competitive performance compared to existing literature. Evaluation metrics include RMSD, GDT, and processing time.
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