The use of CT scan to diagnose kidney stones is among the most accurate ways to confirm the presence of kidney stones in patients. The scan takes photographs inside the body using a computer and an X-ray. The present ...
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The use of CT scan to diagnose kidney stones is among the most accurate ways to confirm the presence of kidney stones in patients. The scan takes photographs inside the body using a computer and an X-ray. The present study proposes a new automatic methodology using an integrated Alexnet and ELM (Extreme Learning Machine) network to deliver more useful outcomes of detection for kidney stone. Afterward, the network is optimized on the basis of a newly improved version of firebug swarm optimization algorithm. The designed network is applied to the "CT Kidney Dataset", and its outcomes are then verified by some different advanced procedures. The final results indicated that the proposed approach has better performance than the other methods.
Since large-scale multi-objective problems (LSMOPs) have huge decision variables, the traditional evolutionary algorithms are facing difficulties of low exploitation efficiency and high exploration costs in solving LS...
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Since large-scale multi-objective problems (LSMOPs) have huge decision variables, the traditional evolutionary algorithms are facing difficulties of low exploitation efficiency and high exploration costs in solving LSMOPs. Therefore, this paper proposes an evolutionary strategy based on two- stage accelerated search optimizers (ATAES). Specifically, a convergence optimizer is devised in the first stage, while a three-layer lightweight convolutional neural network model is built, and the population is homogenized into two subsets, the diversity subset, and the convergence subset, which serve as input nodes and the expected output nodes of the neural network, respectively. Then, by constantly backpropagating the gradient, a satisfactory individual will be produced. Once exploitation stagnation is discovered in the first phase, the second phase will be run, where a diversity optimizer using a differential optimization algorithm with opposite learning is suggested to increase the exploration range of candidate solutions and thereby increase the population's diversity. Finally, to validate the algorithm's performance, on multi-objective LSMOP and DTLZ benchmark suits with decision variable quantities of 100, 300, 500, and 1000, the ATAES demonstrated its superiority with other advanced multi-objective evolutionary algorithms.
The papers discuss the future of high-speed rail transport in the Czech Republic and its accessibility within a specific region (Vysočina). Better accessibility could encourage residents to use public rail transport i...
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The papers discuss the future of high-speed rail transport in the Czech Republic and its accessibility within a specific region (Vysočina). Better accessibility could encourage residents to use public rail transport instead of private car transportation. To achieve this, an algorithm is being developed to integrate future high-speed line RS1 with regional line No. 240 in the Vysočina region without affecting the already planned future high-speed concept or regional rail infrastructure. However, this integration has not resulted in a significant reduction in travel time on the regional part. Other segments of the algorithm focus on upgrading rail infrastructure and adjusting future high-speed train lines and their routing and transfer capabilities, which will be detailed in future publications.
The four-way shuttle system, known for its high level of automation and strong handling capacity, is widely used in flexible automated warehousing systems. To enhance operational efficiency, this study establishes an ...
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This study explores Darcy Forchheimer tri-hybrid nanofluid (CuO +Au +Fe3O4) in blood for drug delivery function in a time dependent squeezed channel with the impacts of MHD, heat generation/absorption, non-linear ther...
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This study explores Darcy Forchheimer tri-hybrid nanofluid (CuO +Au +Fe3O4) in blood for drug delivery function in a time dependent squeezed channel with the impacts of MHD, heat generation/absorption, non-linear thermal radiation. The novelty of the current study is to examine chemical reaction, Brownian and thermophoretic diffusions using the Buongiorno model. The range of CuOfrom 30 % to 50 %, range of Aufrom 20 % to 40 % and 20 % to 35 % for Fe3O4 respectively, have been selected to obtain optimal particles concentrations in ternary hybrid nanofluid. The inclusion of nanoparticlesCuO, Auand Fe3O4 in a base fluid blood predicts a potential reduction in blood viscosity by 10 % to 25 %, and a 15 % to 30 % enhancement in thermal conductivity compared to untreated blood. Also predicts a sustained drug release profile with an estimated delivery efficiency of 60 % to 80 % over a specified period. The tri-hybrid nanofluid increases the localized temperature from 1 degrees C to 3 degrees C within the targeted regions, demonstrating its potential for hyperthermia treatment in theoretical scenarios. The study employs a multilayer perceptron (MLP) neural network with Levenberg-Marquardt backpropagation, utilizing bvp4c numerical data, and incorporating physical constraints like chemical reactions, Prandtl number, and porosity. Furthermore, these parameters are for the range of different scenarios about neural network mapping and the solution. The benchmark datasets are allocated as 70 % for training, 15 % for testing, and 15 % for validation. The results demonstrated the model's effectiveness across all phases of evaluation, achieving highly accurate predictions with minimal error values. This research highlights the potential of tri-hybrid nanofluids for enhancing drug delivery and hyperthermia treatments, offering novel insights into fluid dynamics and biomedical applications.
A common depiction for biological signaling networks is the influence graph in which the activation and inhibition effects between molecular species are shown with vertices and arcs connecting them. Another formalism ...
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A common depiction for biological signaling networks is the influence graph in which the activation and inhibition effects between molecular species are shown with vertices and arcs connecting them. Another formalism for reaction-based models is the Petri nets which has a graphical representation and a mathematical notation that enables structural analysis and quantitative simulation. In this paper, we present an algorithm based on Petri nets topological features for the transformation of the computational model of a biological signaling network into an annotated influence graph. We also show the transformation of the Petri nets model of the beta-adrenergic receptor activating the PKA-MAPK signaling network into its representation as an influence graph.
A multitude of methodologies, based on the detection of cyclostationary features (CFD), are available to researchers seeking to identify unoccupied spectrum channels within cognitive radio (CR) networks. Notwithstandi...
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A multitude of methodologies, based on the detection of cyclostationary features (CFD), are available to researchers seeking to identify unoccupied spectrum channels within cognitive radio (CR) networks. Notwithstanding the inherent difficulties of wireless environments, such as those involving a low signal-to-noise ratio, CFD has demonstrated considerable potential. However, addressing signal variation and system complexity remains a primary area of research. This paper introduces a novel CFD algorithm that employs the autocorrelation function as a preprocessing step to enhance the received signal characteristics and distinguish noisy signals from noise. Subsequently, a straightforward cyclostationary detection approach is applied. The objective of this blind cyclostationary spectrum detection technique was to reduce the algorithmic complexity and enhance the detection efficiency. This paper presents optimized parameters for a blind cyclostationary detector and offers an evaluation of its performance in simulation environments. The results demonstrate a minimum 3.5 dB enhancement in detection performance relative to the benchmarking techniques. Furthermore, the SDR implementation of the proposed method in the receiving part of a transmitter/receiver FM broadcasting system, using two USRPs cards connected to two laptops running the GNU Radio platform, serves to validate its effectiveness in real-time scenarios.
Introduction and Objectives: TIPS placement is an effective, possibly life-saving, treatment for complications of portal hypertension. The pressure shift induced by the stent can lead to cardiac decompensation (CD). W...
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Introduction and Objectives: TIPS placement is an effective, possibly life-saving, treatment for complications of portal hypertension. The pressure shift induced by the stent can lead to cardiac decompensation (CD). We investigated the incidence of CD, possible variables associated with CD and the validity of the Toulouse algorithm for risk prediction of CD post-TIPS. Patients and Methods: A total of 106 patients receiving TIPS for variceal bleeding (VB, 41.5%) or refractory ascites (RA, 58.5%) with available echocardiography and NT-proBNP results were included and retrospectively reviewed. Development of CD between time of TIPS placement and occurrence of liver transplantation, death or loss-to-follow-up was recorded. Competing risk regression analysis was performed to assess which baseline variables predicted occurrence of CD post-TIPS. Results: A total of 12 patients (11.3%) developed CD after a median of 11.5 days (IQR 4 to 56.5) post-TIPS. Multivariate regression showed age (HR 1.06, p = 0.019), albumin (HR 1.10, p = 0.009) and NT-proBNP (HR 1.00, p = 0.023) at baseline predicted CD in the RA group. No clear predictors were found in those receiving TIPS for VB. Correspondingly, the Toulouse algorithm successfully identified patients at risk for CD, however only in the RA population (zero risk 0% vs. low risk 12.5% vs. high risk 35.3% with CD;p = 0.003). Conclusions: CD is not an infrequent complication post-TIPS occurring in 1/10 patients. The Toulouse algorithm can identify patients at risk of CD, though only in patients receiving TIPS for RA. Allocation to the highrisk category warrants close monitoring but should not preclude TIPS placement. (c) 2024 Fundaci & oacute;n Cl & iacute;nica M & eacute;dica Sur, A.C. Published by Elsevier Espa & ntilde;a, S.L.U. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/)
In order to maximize humanoid robot navigation, this paper introduces the Enhanced DAYANI Arc Contour Intelligent (EDACI) Method, which integrates Dynamic Window Approach (DWA) to choose the best walking parameters fo...
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In order to maximize humanoid robot navigation, this paper introduces the Enhanced DAYANI Arc Contour Intelligent (EDACI) Method, which integrates Dynamic Window Approach (DWA) to choose the best walking parameters for avoiding obstacles and smooth trajectory management. EDACI algorithm provides the best response to guide humanoid robots to the goal by avoiding obstacles and preparing a smooth trajectory. Further, DWA optimizes the walking pattern of humanoid robots by controlling their velocity while encountering an obstacle and finding a smooth trajectory. The performance of the proposed controller is examined by implementing it in humanoid NAOs for navigation in several simulated and experimental terrains. It is implemented on a single humanoid robot for navigation in static and dynamic environments and on multiple humanoid robots on a single platform. Navigation of multiple robots has to deal with the situation of conflict where one robot behaves as a dynamic obstacle to the other. It is solved by setting a Dining Philosopher Controller (DPC) in the base technique. The results obtained from the simulations and experiments have a divergence below 5 %, which demonstrates a satisfactory relation between them. The proposed controller's efficacy is demonstrated by comparing the torque developed at different joints with contrast to the inbuilt controller of NAO. The results show good improvement in torque produced at all joints. In addition, it is compared with an existing controller for navigation, which displays superiority of the proposed controller.
This paper proposed a new partition-based clustering algorithm inspired by the sand dunes. The proposed algorithm is called the Sand Dune Clustering algorithm. The algorithm accepts an r x c matrix of data where one o...
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ISBN:
(纸本)9798400717383
This paper proposed a new partition-based clustering algorithm inspired by the sand dunes. The proposed algorithm is called the Sand Dune Clustering algorithm. The algorithm accepts an r x c matrix of data where one of its columns should be identified as the weight. The weight of the column is then utilized to cluster the data points. Based on initial experimentation, the algorithm was able to group the sample points. Furthermore, it was disclosed that the algorithm is relatively fast, running at the order O(rc + r). Thus, it can be concluded that the proposed algorithm is a promising clustering algorithm. Limitations and future work are also discussed.
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