In this paper, we construct n-ary block codes induced by a hyper BCK-valued function and provide a method which allows us to find a hyper BCK-algebra starting from an n-ary block code. Since doing calculus by hand in ...
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In this paper, we construct n-ary block codes induced by a hyper BCK-valued function and provide a method which allows us to find a hyper BCK-algebra starting from an n-ary block code. Since doing calculus by hand in finite hyper BCK-algebras is really tedious work, we design algorithms for studying properties of hyper BCK-algebras.
Essential Information about algorithms and Data Structures A Classic Reference The latest version of Sedgewick,s best-selling series, reflecting an indispensable body of knowledge developed over the past several deca...
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ISBN:
(纸本)9780321573513
Essential Information about algorithms and Data Structures A Classic Reference The latest version of Sedgewick,s best-selling series, reflecting an indispensable body of knowledge developed over the past several decades. Broad Coverage Full treatment of data structures and algorithms for sorting, searching, graph processing, and string processing, including fifty algorithms every programmer should know. See
In this paper, algorithms for calculating different types of generalized trigonometric and hyperbolic functions are developed and presented. The main attention is focused on the Ateb-functions, which are the inverse f...
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In this paper, algorithms for calculating different types of generalized trigonometric and hyperbolic functions are developed and presented. The main attention is focused on the Ateb-functions, which are the inverse functions to incomplete Beta-functions. The Ateb-functions can generalize every kind of implementation where trigonometric and hyperbolic functions are used. They have been successfully applied to vibration motion modeling, data protection, signal processing, and others. In this paper, the Fourier transform's generalization for periodic Ateb-functions in the form of Ateb-transform is determined. Continuous and discrete Ateb-transforms are constructed. algorithms for their calculation are created. Also, Ateb-transforms with one and two parameters are considered, and algorithms for their realization are built. The quantum calculus generalization for hyperbolic Ateb-functions is constructed. Directions for future research are highlighted.
Many sciences exploit algorithms in a large variety of applications. In agronomy, large amounts of agricultural data are handled by adopting procedures for optimization, clustering, or automatic learning. In this part...
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Many sciences exploit algorithms in a large variety of applications. In agronomy, large amounts of agricultural data are handled by adopting procedures for optimization, clustering, or automatic learning. In this particular field, the number of scientific papers has significantly increased in recent years, triggered by scientists using artificial intelligence, comprising deep learning and machine learning methods or bots, to process field, crop, plant, or leaf images. Moreover, many other examples can be found, with different algorithms applied to plant diseases and phenology. This paper reviews the publications which have appeared in the past three years, analyzing the algorithms used and classifying the agronomic aims and the crops to which the methods are applied. Starting from a broad selection of 6060 papers, we subsequently refined the search, reducing the number to 358 research articles and 30 comprehensive reviews. By summarizing the advantages of applying algorithms to agronomic analyses, we propose a guide to farming practitioners, agronomists, researchers, and policymakers regarding best practices, challenges, and visions to counteract the effects of climate change, promoting a transition towards more sustainable, productive, and cost-effective farming and encouraging the introduction of smart technologies.
Background: Artificial Intelligence (AI) is transforming drug development and clinical trials, helping researchers find new treatments faster and personalize care for patients. By automating tasks like molecule screen...
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Background: Artificial Intelligence (AI) is transforming drug development and clinical trials, helping researchers find new treatments faster and personalize care for patients. By automating tasks like molecule screening and predicting treatment outcomes, AI addresses critical challenges in modern medicine. Objectives: This review explores how AI is being used in drug development and clinical trials, focusing on its benefits, limitations, and potential to improve healthcare outcomes. Methods: A scoping review based on Arksey and O'Malley's, 2005 framework was conducted, analyzing 1,956 studies from PubMed, Web of Science, IEEE Xplore, and Scopus. Ten studies were selected for in-depth analysis. Results: Common AI techniques include Support Vector Machines, Neural Networks, and Random Forests, applied in tasks such as identifying new drug uses, predicting antibiotic resistance, and streamlining clinical trials. While AI has shown great promise, challenges like inconsistent data quality and difficulties in clinical validation remain. Conclusions: AI offers exciting opportunities to improve healthcare by making drug development and clinical trials more efficient. However, overcoming barriers like data integration and methodological standardization is essential to ensure these tools benefit diverse populations, especially in settings like Brazil, where genetic diversity and health inequalities pose unique challenges.
Cancer remains a formidable global health challenge, necessitating the development of innovative diagnostic techniques capable of early detection and differentiation of tumor/cancerous cells from their healthy counter...
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Cancer remains a formidable global health challenge, necessitating the development of innovative diagnostic techniques capable of early detection and differentiation of tumor/cancerous cells from their healthy counterparts. This review focuses on the confluence of advanced computational algorithms with noninvasive, label-free impedance-based biophysical methodologies-techniques that assess biological processes directly without the need for external markers or dyes. This review elucidates a diverse array of state-of-the-art impedance-based technologies, illuminating distinct electrical signatures inherent to cancer vs healthy tissues. Additionally, the study probes the transformative potential of these diagnostic modalities in recalibrating personalized cancer treatment paradigms. These modalities offer real-time insights into tumor dynamics, paving the way for precision-guided therapeutic interventions. By emphasizing the quest for continuous in vivo monitoring, these techniques herald a pivotal advancement in the overarching endeavor to combat cancer globally.
Tensor decomposition is widely used for multi-way data analysis and computations in chemical science. CP decomposition is one of the most useful tensor decomposition models for capturing the essential information in m...
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Tensor decomposition is widely used for multi-way data analysis and computations in chemical science. CP decomposition is one of the most useful tensor decomposition models for capturing the essential information in massive multi-way chemical data and for efficiently performing computations with such tensors. However, efficiently and accurately computing the tensor decomposition itself is a nontrivial problem that sometimes limits the advantage of tensor decomposition methods. In this work we propose and test three new decomposition algorithms, that are defined from extrapolation ideas applied to the alternating least square (ALS) algorithm for CP tensor decomposition. The performance of the proposed algorithms are validated on both a variety of simulated datasets and real experimental datasets including fluorescence spectroscopy data, hyperspectral data and electroencephalogram (EEG) data. The results show that the proposed algorithms significantly accelerate the standard CP-ALS decomposition while maintaining favorable accuracy. One of the proposed methods, denoted direct inversion of the iterative subspace-like extrapolated ALS(CP-AD), is inspired from widely used extrapolation procedures used in the context of solving non-linear equations in quantum chemistry, and shows a particular attractive combination of a much reduced number of iterations needed for convergence, and modest computational cost. For example, CP-AD provided resulting tensors of similar accuracy but significantly lower computational cost than the standard CP-ALS algorithm and the widely used line-search based CP-ALS extrapolation procedure. The proposed methodology may thereby boost the application of tensor decomposition modeling in both experimental and computational chemistry.
Atrial fibrillation (AF) is one of the frequent and potentially dangerous arrhythmias that can participate in cardioembolic stroke and heart failure. Early AF identification is possible by the combination of algorithm...
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Atrial fibrillation (AF) is one of the frequent and potentially dangerous arrhythmias that can participate in cardioembolic stroke and heart failure. Early AF identification is possible by the combination of algorithms with wearable technology, which makes it easier to transform from hospital-based to at-home care for AF detection. This review presents an overview of the combination of intelligent algorithms with smart devices for AF discrimination. The smart devices are summarized in detail. Then, an extensive discussion of AF detection algorithms in three key aspects including database, feature extraction, and classification algorithms, is elaborated. Furthermore, the integration of intelligent algorithms with wearable technology for effective AF monitoring is systematically interpreted. Lastly, the challenges and outlook of smart devices enabled by AF screening algorithms are also discussed. This review aims to provide a comprehensive understanding of AF screening utilizing wearable devices enabled by algorithms.
The current work aims to develop computational models for the thermal characteristics of turbulent CH4flames for varying burner dimensions. This study develops a platform for data-driven analysis of temperature predic...
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The current work aims to develop computational models for the thermal characteristics of turbulent CH4flames for varying burner dimensions. This study develops a platform for data-driven analysis of temperature prediction of turbulent non-premixed flames, in which the influence of flow and geometric parameters, including burner head diameter (D), half cone angles (α), and co-flow air velocity (Ucf), have been considered. The algorithms used were ridge regressor (RR), linear regressor (LR), and three variations of support vector regression (SVR): SVR with a linear kernel (SVR-LR), SVR with a radial basis function (SVR-RBF), and SVR with a polynomial kernel (SVR-Poly). The performance of each computational model was evaluated and contrasted based on several metrics: mean absolute error, regression coefficient (R2), mean absolute percentage error, and mean Poisson deviance. From the modeling of the output data, it was observed that the SVR-RBF predictions were more accurate compared to those from the other algorithms, as it achieved the highest training value of 0.955. The testing predictions of RR, SVR-LR, SVR-RBF, and SVR-Poly algorithms were also robust, withR2values ranging between 0.91 and 0.94. It is, therefore, established that these computational models are effectively suited for predicting sensitive turbulent CH4flame characteristics based on varying input factors.
Vocalizations can vary as a function of their context of production and provide an immediate measure of an animal's affective states. If vocal expression of emotions has been conserved throughout evolution, direct...
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Vocalizations can vary as a function of their context of production and provide an immediate measure of an animal's affective states. If vocal expression of emotions has been conserved throughout evolution, direct between-species comparisons using the same set of acoustic indicators should be possible. The present study used a machine learning algorithm (eXtreme Gradient Boosting [XGBoost]) to distinguish between contact calls indicating positive (pleasant) and negative (unpleasant) emotional valence, produced in various contexts by seven species of ungulates. With an accuracy of 89.49% (balanced accuracy: 83.90%), we found that the most important predictors of emotional valence were acoustic variables reflecting changes in duration, energy quartiles, fundamental frequency, and amplitude modulation. This approach is critical in the field of emotional communication, where more information is needed to reach a better understanding of the emotional origins of human language. In addition, these results can help toward the development of automated tools for animal well-being monitoring.
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