Hyperpolarized gas (HPG) magnetic resonance (MR) imaging allows for the quantification of pulmonary defects with the ventilation defect percentage (VDP). Although informative, VDPs lack information regarding the spati...
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Hyperpolarized gas (HPG) magnetic resonance (MR) imaging allows for the quantification of pulmonary defects with the ventilation defect percentage (VDP). Although informative, VDPs lack information regarding the spatial distribution of defects. We developed a method of quantifying the focality/sparseness of ventilation defects in hyperpolarized-gas lung MR images. The study involved a total of 56 subjects: 14 asthmatics (age mean +/- sd = 45.1 +/- 18.9), 25 COPD subjects (age = 60.6 +/- 10.4), and 17 CF subjects (age = 21.8 +/- 8.4). The analyzed data are from four different studies: Study 1 used a 3-T gradient echo (GRE) sequence, Study 2 used a 1.5-T GRE sequence, Study 3 used a 1.5-T two-dimensional spiral sequence, and Study 4 used a 1.5-T three-dimensional balanced steady-state free precession (bSSFP) sequence. We developed an algorithm that quantifies the focality/sparseness of ventilation defects as a subject's cluster index (CI). The algorithm was assessed on synthesized spherical defect clusters and 3D lung volume defects of varying sizes/distributions. CI and whole-lung VDP were calculated for asthmatic, COPD, and CF subjects. Pearson correlation coefficients and linear regression models between CI and FEV1pp, as well as CI and VDP, were performed to evaluate CI among asthma, COPD, and CF. T tests were performed to evaluate CI/VDP ratios among previously mentioned lung diseases. p values less than 0.05 were statistically significant. Subject CI well represents defect focality by visual inspection. Pearson correlation coefficients between CI and VDP were r = 0.60 (p = 2.21 x 10-2) for asthma, r = 0.79 (p = 3.15 x 10-6) for COPD, and r = 0.84 (p = 2.80 x 10-5) for CF. Pearson correlation coefficients between CI and FEV1pp was r = -0.47 (p = 0.0002). Analysis of variance (ANOVA) and a Tukey's honestly significant difference (HSD) test revealed that the ratio of whole-lung CI/VDP was significantly different between asthma/CF (p = 0.04) and CF/COPD (p = 0.008),
Considering the exponential growth of network traffic, particularly driven by over-the-top (OTT) streaming applications, video category network traffic constitutes a significant portion of overall network traffic. How...
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Considering the exponential growth of network traffic, particularly driven by over-the-top (OTT) streaming applications, video category network traffic constitutes a significant portion of overall network traffic. However, most research has focused on the categorization and diversity of network traffic using benchmark datasets, with limited attention paid to video category network traffic. Additionally, there is a lack of proprietary Internet video traffic datasets, and the few proprietary datasets available often lack transparency and interpretability. This paper introduces a novel framework for generating proprietary Internet video traffic datasets, addressing existing gaps in dataset quality and consistency. We propose the nYFTQC algorithm, which enables the creation of fifteen detailed datasets specifically designed for Internet video traffic analysis. The proposed datasets demonstrate superior performance metrics, including completeness, consistency, and transparency. This comprehensive approach enhances the accuracy and interpretability of traffic sample analysis, providing valuable resources for future research in video category network traffic.
Deaerator is a key equipment in the secondary circuit system, its operating parameters and structural size have a significant impact on the thermal efficiency and arrangement rationality of the system. In this researc...
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Deaerator is a key equipment in the secondary circuit system, its operating parameters and structural size have a significant impact on the thermal efficiency and arrangement rationality of the system. In this research, a mathematical model of the marine nuclear power deaerator is established, and the influence of thermal and structural parameters on the weight and volume of the deaerator is analyzed. An improved particle swarm optimization algorithm is proposed by introducing Tent chaotic mapping, evolutionary factor and Metropolis criterion, and its performance is verified. Taking the weight and volume minimization of the deaerator as the optimization objectives, the optimal design of the deaerator is carried out using the proposed IPSO algorithm, while satisfying the structural and performance constraints. The optimization results show that the volume and weight of the deaerator can be reduced by 12.979% and 10.213%, respectively, and the feasibility of the optimization design method is proved theoretically.
Of all meteorological events, Tropical Cyclones (TCs) are by far the costliest of natural hazards around the globe. They typically lose their strength quite rapidly once making landfall. Recent studies have revealed t...
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Of all meteorological events, Tropical Cyclones (TCs) are by far the costliest of natural hazards around the globe. They typically lose their strength quite rapidly once making landfall. Recent studies have revealed that TCs, even degrading below TC strength after landfall, can survive for prolonged periods and still exert a significant impact as Post-Tropical Cyclones (PTCs). However, the widely used TC best track datasets, including the International Best Track Archive for Climate Stewardship, do not consistently track TCs for long enough following landfall to include complete PTC tracks. The absence of tracking limits our understanding of the overall TC-related impacts. In this study, we developed a semi-automatic tracking algorithm using satellite imagery and reanalysis data to extend TC tracks beyond the best track dataset until dissipation overland. Based on all landfalling TCs for the period 1990-2020 in Australia, these TCs can be further tracked overland for an additional 1.6 days on average, with a maximum of 15 days, since the last record in best track datasets. Although the intensity of Australian landfalling TCs has declined over the 30 years, they continue to linger over land for similar durations before dissipation, suggesting an increasing likelihood of favorable land conditions for TCs and PTCs.
Among the phases constituting analog circuit design, circuit sizing is considered labor-intensive, formidable, and heavily experience-dependent due to its non-linearity. Asa result, design automation coupled with effe...
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Among the phases constituting analog circuit design, circuit sizing is considered labor-intensive, formidable, and heavily experience-dependent due to its non-linearity. Asa result, design automation coupled with effective optimization techniques has arisen as a feasible candidate to address challenges with circuit design and satisfy the increasing need for high-performance circuits. Among evolutionary algorithms, the combination of the genetic algorithm (GA) and quantum computing techniques has yielded the hybrid quantum genetic algorithm (HQGA) which has proven to be an effective optimization method in many fields due to its convergence rate and near-optimal solutions. This paper introduces an upgraded version of HQGA we call the Auto-adjusting Hybrid Quantum Genetic algorithm (AHQGA) which avoids premature convergence and improves convergence speed through the use of an additional best-fitness-based scheme for rotation angles. In particular, this work proposes the utility of AHQGA for the multi-objective optimization of analog circuit sizing, with the two- stage Miller-compensated operational amplifier (op-amp) used as a topological case study. Additionally, for an objective evaluation, optimization results by AHQGA are compared with those by HQGA with fixed rotation angles and classical GA.
In this paper, the steady-state optimization problem of S-type biochemical systems is investigated. A mathematical model of the S-type biochemical system is given. A nonconvex bi-objective optimization model with stab...
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In this paper, the steady-state optimization problem of S-type biochemical systems is investigated. A mathematical model of the S-type biochemical system is given. A nonconvex bi-objective optimization model with stability guarantees is constructed with the optimization objectives of maximizing the product yield and minimizing the sum of metabolite concentrations, which can ensure that the S-type biochemical system operates at an optimal and stable steady state. The nonconvex bi-objective optimization model is transformed into a nonconvex single-objective optimization problem based on the normal-boundary intersection (NBI) method. In order to efficiently solve the transformed nonconvex single-objective problem, a geometric programming algorithm is proposed based on the equivalence transformation and monomial compensation strategy, and the convergence analysis of the algorithm is given. An iterative solution algorithm for nonconvex bi-objective optimization model with stability guarantees is given based on this algorithm. The Pareto optimal solutions and Pareto optimal fronts of three different nonconvex bi-objective optimization models are obtained by computing three examples of S-type biochemical systems.
Natural disasters, such as floods and fires, affect various regions of the world every year. One of the most critical aspects of disaster management and planning is facilitating the evacuation of people. Therefore, th...
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Natural disasters, such as floods and fires, affect various regions of the world every year. One of the most critical aspects of disaster management and planning is facilitating the evacuation of people. Therefore, this study develops a mathematical model for emergency evacuation, taking into account the constraints and limitations of transferring individuals to shelters. The model, called the maximal bus evacuation planning, considers constraints including road blockages, vehicle fuel, and passenger capacities. Given the Nondeterministic Polynomial-time hard complexity of this problem, a Hybrid Particle Swarm Optimization-Heuristic algorithm, which combines the Particle Swarm Optimization algorithm with a Heuristic algorithm, is used to solve the nonlinear model. Furthermore, we linearize the model and solve it using the developed Combinatorial Benders' Cuts approach in three versions. Numerical computations are examined under various scenarios, and the outputs of the Hybrid Particle Swarm Optimization - Heuristic algorithm reach suitable solutions within a short timeframe. Additionally, the results from solving the linearized model using the Combinatorial Benders' Cuts show that, in most cases, the execution time of the second version is better than the other versions.
This study presents an innovative approach to optimize energy management in residential microgrids, in light of the rising demand for energy and mounting environmental concerns. The research underscores the vital role...
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This study presents an innovative approach to optimize energy management in residential microgrids, in light of the rising demand for energy and mounting environmental concerns. The research underscores the vital role of efficient energy management and responsive load control to improve energy efficiency and reduce consumer costs. To this end, a framework is proposed in which a power aggregator operates within a microgrid to manage residential electricity consumption. The primary goal of this framework is to minimize energy costs while considering subscriber preferences and the capacity limitations of the distribution network. The improved particle swarm optimization (IPSO) algorithm is employed to optimize energy management, resolve convergence challenges, and ensure user requirements are effectively prioritized. Integrating emergency, economic, and planned strategies provides cost savings, ensures grid stability, and enhances user satisfaction. The incorporation of Internet of Things (IoT) technology enables seamless communication, precise device control, and data-driven decision-making, empowering households to manage their energy loads more effectively and contribute to grid efficiency. Through scenario analysis, this research demonstrates the IPSO algorithm's potential for significant cost reductions and improved grid stability. In Scenario 1, focused exclusively on affordability, numerical analyses present the total cost of electricity under different load conditions over three months. Scenario 2, also prioritizing affordability, highlights the impact of economic considerations on electricity expenses. Furthermore, Scenario 3 (80 % emergency + 20 % affordable) and Scenario 4 (50 % emergency + 20 % affordable + 30 % planned) showcase the potential for cost reduction through various priority combinations. These insights reflect the effectiveness of load management strategies facilitated by IoT technology. This comprehensive energy management approach lays a stro
A new algorithm for optimizing thermodynamic parameters of binary systems is introduced. Unlike traditional algorithms that are commonly single-objective and need extensive manual testing, this algorithm constructs a ...
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A new algorithm for optimizing thermodynamic parameters of binary systems is introduced. Unlike traditional algorithms that are commonly single-objective and need extensive manual testing, this algorithm constructs a multi-objective optimization for different types of experimental data. Then, by using weighted sum method, the multi-objective optimization problem is transformed into a single-objective optimization, which is solved by Barzilai-Borwein method. The key advantage of this algorithm is that no restrictions on the selection of initial values are needed. Finally, this algorithm is applied to optimize the thermodynamic parameters in the Ag-Pd and La-C systems. The experimental phase diagrams and thermodynamic properties in these two systems are satisfactorily reproduced by the present calculation.
A hybrid approach for brain magnetic resonance imaging (MRI) image segmentation is proposed, that combines the intuitionistic particle swarm optimization (IPSO) algorithm and the kernel intuitionistic fuzzy entropy cm...
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A hybrid approach for brain magnetic resonance imaging (MRI) image segmentation is proposed, that combines the intuitionistic particle swarm optimization (IPSO) algorithm and the kernel intuitionistic fuzzy entropy cmeans (KIFECM) to overcome the local minima trapping problem of KIFECM. In terms of intuitionistic fuzzification, intuitionistic PSO (IPSO) is a global optimization technique that investigates random search with candidate solution. The hybrid KIFECM-IPSO technique combines the best aspects of IPSO and KIFECM algorithms to improve clustering outcomes and prevent the local minima entrapment issue. The performance of the algorithm can be evaluated using various measures such as the similarity measure, average similarity measure, jaccard coefficient, false negative ratio and false positive ratio on the real brain data and simulated brain data. The results have been compared with KIFECM, PSO-KIFCM, FEC, PSO and FCM-PSO algorithms to determine the effectiveness of the proposed algorithm and the experimental results shows that the proposed KIFECM-IPSO perform better. Friedman's statistical test is also carried out to demonstrate that the performance of the proposed algorithm is not only better but the performance difference is statistically significant also.
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