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 ...
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This paper investigates the effect of low-resolution analog-to-digital converters (ADCs) on device activity detection in massive machine-type communications (mMTC). The low-resolution ADCs induce two challenges on the...
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Androgenetic alopecia (AGA) is a common cause of hair loss affecting both men and women. Although its precise etiology remains uncertain, genetic and hormonal factors are recognized as major contributors. This study i...
Androgenetic alopecia (AGA) is a common cause of hair loss affecting both men and women. Although its precise etiology remains uncertain, genetic and hormonal factors are recognized as major contributors. This study introduces a computational intelligence framework employing fuzzy logic and multi-criteria decision-making (MCDM) to simulate a robust triage system for AGA management. Using a simulated dataset of 100 AGA patients, we applied the fuzzy-weighted zero-inconsistency (FWZIC) method to assign weights to 11 bioactive criteria associated with AGA. These weights informed a novel triage procedure for alopecia patients (TPAP), which stratified patients into seven severity levels (level 1: minor; level 7: severe). This study presents a computationally intelligent triage model tailored for AGA, emphasizing the applicability of fuzzy MCDM techniques in medical decision support. The TPAP framework can assist in resource allocation and treatment planning, paving the way for personalized and timely interventions in hair loss management.
Quantum entanglement is so fundamentally different from a network packet that several quantum network stacks have been proposed;one of which has even been experimentally demonstrated. Several simulators have also been...
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Deep reinforcement learning has proven remarkably useful in training agents from unstructured data. However, the opacity of the produced agents makes it difficult to ensure that they adhere to various requirements pos...
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Research in the field of dynamic behaviors in neural networks with variable-order differences is currently a thriving area, marked by various significant discoveries. However, when it comes to discrete-time neural net...
Research in the field of dynamic behaviors in neural networks with variable-order differences is currently a thriving area, marked by various significant discoveries. However, when it comes to discrete-time neural networks featuring fractional variable-order nonlocal and nonsingular kernels, there has been limited exploration. This paper stands as one of the initial contributions to this subject, focusing primarily on the topics of stability and synchronization in finite-time within discrete neural networks. The research employs the nabla ABC variable-order difference operator, with a primary approach involving the investigation of a novel Gronwall inequality using the Atangana-Baleanu difference variable-order sum operator. This analysis leads to the development of a uniqueness theorem and a criterion for the stability in finite-time of variable-order discrete neural networks. Furthermore, the requirements stemming from this type of stability and the novel Gronwall inequality serve as the foundation for establishing the conditions necessary for achieving finite-time synchronization in these networks, employing a specific control using state feedback method. Finally, the study utilizes numerical solutions to validate the obtained results.
In this contribution, we study the numerical approximation of scalar conservation laws by computational optimization of the numerical flux function in a first-order finite volume method. The cell-averaging inherent to...
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The inspection of products and assessment of quality is connected with high costs and time effort in many industrial domains. This also applies to the forestry industry. Utilizing state-of-the-art deep learning models...
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The goal of future quantum networks is to enable new internet applications that are impossible to achieve using solely classical communication[1, 2, 3]. Up to now, demonstrations of quantum network applications[4, 5, ...
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We address the problem of learning a machine learning model from training data that originates at multiple data owners, while providing formal privacy guarantees regarding the protection of each owner's data. Exis...
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