This article concentrates on the fixed-time stabilization(fS) and fixed-time synchronization(fTS) of discontinuous inertial neural networks(DINNs) with distributed delays. Using mathematical induction to a new differe...
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This article concentrates on the fixed-time stabilization(fS) and fixed-time synchronization(fTS) of discontinuous inertial neural networks(DINNs) with distributed delays. Using mathematical induction to a new differential equality, a novel fixed-time stability lemma is constructed. Then, by designing an aperiodically semi-intermittent switching control and combining it with the theory of nonsmooth analysis, some novel criteria on the fS and fTS of DINNs are obtained. Unlike the methods used in most existing studies, the fS and fTS results are structured using the newly proposed fixed-time stability lemma and the nonreduced-order approach, resulting in broader practical applications and strengthening the scientific quality of the derived results. Lastly, numerical simulations and applications proffer the effectiveness of the established fS and fTS criteria.
The off situ accurate reconstruction of the core neutron field is an important step in realizing real-time reactor *** existing off situ reconstruction method of the neutron field is only applicable to cases wherein a...
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The off situ accurate reconstruction of the core neutron field is an important step in realizing real-time reactor *** existing off situ reconstruction method of the neutron field is only applicable to cases wherein a single region changes at a specified location of the ***,when the neutron field changes are complex,the accurate identification of the individual changed regions becomes challenging,which seriously affects the accuracy and stability of the neutron field ***,this study proposed a dual-task hybrid network architecture(DTHNet)for off situ reconstruction of the core neutron field,which trained the outermost assembly reconstruction task and the core reconstruction task jointly such that the former could assist the latter in the reconstruction of the core neutron field under core complex ***,to exploit the characteristics of the ex-core detection signals,this study designed a global-local feature upsampling module that efficiently distributed the ex-core detection signals to each reconstruction unit to improve the accuracy and stability of *** experiments were performed on the simulation datasets of the CLEAR-I reactor to verify the accuracy and stability of the proposed *** results showed that when the location uncertainty of a single region did not exceed nine and the number of multiple changed regions did not exceed ***,the reconstructed ARD was within 2%,RD_(max)was maintained within 17.5%,and the number of RD≥10%was maintained within ***,when the noise interference of the ex-core detection signals was within±2%,although the average number of RD≥10%increased to 16,the average ARD was still within in 2%,and the average RD_(max)was within 22%.Collectively,these results show that,theoretically,the DTHNet can accurately and stably reconstruct most of the neutron field under certain complex core changes.
Pelagic fish are the most abundant species in upwelling regions,contributing 25%of total global fisheries ***-driven changes in the marine environment play a crucial role in their population *** Chilean jack mackerel(...
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Pelagic fish are the most abundant species in upwelling regions,contributing 25%of total global fisheries ***-driven changes in the marine environment play a crucial role in their population *** Chilean jack mackerel(Trachurus murphyi)as an example,this study conducted simulations to quantify the impacts of environmental variations on the stock assessment.A habitat-based surplus production model was developed by integrating suitable habitat area into the model parameters carrying capacity(K)and intrinsic growth rate(r),with a suitable habitat area serving as the proxy for the environmental conditions for Chilean jack mackerel in the Southeast Pacific *** dynamics of Chilean jack mackerel stock and fisheries data were simulated,and four assessment models with different configurations were built to fit simulated data,with or without considering environmental *** results indicated that Joint K-r model,which integrated both parameters with the suitable habitat area index,outperformed the others by coming closest to the‘true'population *** habitat variations in the estimation model tended to overestimate biomass and underestimate harvest rate and reference *** observation and process error,the results were estimated with bias,while fMSY is relatively *** research illustrates the importance to consider random errors and environmental influences on populations,and provides foundation guidelines for future stock assessment.
Anaerobic digestion(AD)is widely employed for sludge stabilization and waste ***,the slow hydrolysis process hinders methane production and leads to prolonged sludge *** this study,an efficient and eco-friendly lysozy...
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Anaerobic digestion(AD)is widely employed for sludge stabilization and waste ***,the slow hydrolysis process hinders methane production and leads to prolonged sludge *** this study,an efficient and eco-friendly lysozyme pre-treatment method was utilized to address these *** optimizing lysozyme dosage,hydrolysis and cell lysis were ***,lysozyme combined with hydrothermal pretreatment enhanced overall *** indicate that:(1)When lysozyme dosage reached 90 mg/g TS after 240 min of pretreatment,SCOD,soluble polysaccharides,and protein content reached their maxima at 855.00,44.09,and 204.86 mg/L,*** represented an increase of 85.87%,365.58%,and 259.21%compared to the untreated *** fluorescence spectroscopy revealed the highest fluorescence intensity in the IV region(soluble microbial product),promoting microbial metabolic activity.(2)Lysozyme combined with hydrothermal pretreatment significantly increased SCOD,soluble proteins,and polysaccharide release from sludge,reducing SCOD release *** experiments identified group 3 as the most effective for SCOD and soluble polysaccharide release,while group 9 released the most soluble *** significance order of factors influencing SCOD,soluble proteins,and polysaccharide release is hydrothermal temperature>hydrothermal time>enzymatic digestion time.(3)The lysozyme-assisted hydrothermal pretreatment group exhibited the fastest release and the highest SCOD concentration of 8,135.00 mg/L during anaerobic *** SCOD consumption and cumulative gas production increased by 95.89%and 130.58%,respectively,compared to the control group,allowing gas production to conclude 3 days earlier.
In the face of data scarcity in the optimization of maintenance strategies for civil aircraft,traditional failure data-driven methods are encountering challenges owing to the increasing reliability of aircraft *** stu...
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In the face of data scarcity in the optimization of maintenance strategies for civil aircraft,traditional failure data-driven methods are encountering challenges owing to the increasing reliability of aircraft *** study addresses this issue by presenting a novel combined data fusion algorithm,which serves to enhance the accuracy and reliability of failure rate analysis for a specific aircraft model by integrating historical failure data from similar models as supplementary *** a comprehensive analysis of two different maintenance projects,this study illustrates the application process of the *** upon the analysis results,this paper introduces the innovative equal integral value method as a replacement for the conventional equal interval method in the context of maintenance schedule *** Monte Carlo simulation example validates that the equivalent essential value method surpasses the traditional method by over 20%in terms of inspection efficiency *** discovery indicates that the equal critical value method not only upholds maintenance efficiency but also substantially decreases workload and maintenance *** findings of this study open up novel perspectives for airlines grappling with data scarcity,offer fresh strategies for the optimization of aviation maintenance practices,and chart a new course toward achieving more efficient and cost-effective maintenance schedule optimization through refined data analysis.
The Earth's magnetosphere is a region occupied by many artificial satellites, and also is a region that spacecraft must cruise during the deep-space exploration. In this sense, the Earth's magnetosphere is closely rel...
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The Earth's magnetosphere is a region occupied by many artificial satellites, and also is a region that spacecraft must cruise during the deep-space exploration. In this sense, the Earth's magnetosphere is closely related to human activity and is a candidate for us to expand our living space. generally, the Earth's magnetosphere can preclude most energetic particles from the Sun and the interstellar space, effectively protecting human beings on the Earth from being attacked and thus making the Earth to be a habitable planet. However, in some conditions, the Earth's magnetosphere becomes dynamic and energetic, and consequently may damage the artificial satellites, threaten the astronauts' health, and disrupt the ground infrastructure, which leads to a decline in the national economy. Therefore, investigating how energy is injected into the magnetosphere, how it is transported in the magnetosphere, and how it is ultimately dissipated in the magnetosphere are the key issues in space physics. Targeting these key issues, in this paper, we review the recent progress on them. Particularly, we introduce the relevant scientific questions,models, methods, and spacecraft missions, for better building a physical link among the energy injection, transport, and dissipation in the magnetosphere, present an energy chain of the magnetosphere, reveal the relationship between such energy chain and the space weather events, and discuss the forecasting and warning methods for energetic-particle events in the magnetosphere. The magnetospheric energy chain discussed in this paper will help us reveal the mechanisms of space weather events, establish the models of space environment, and forecast the disastrous space weather events.
The Tetrahedral Constellation gravitational Wave Observatory(TEgO)[1]represents a significant leap forward in gravitational wave detection,advancing our understanding ofgravitational *** introducing a fourth spacecra...
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The Tetrahedral Constellation gravitational Wave Observatory(TEgO)[1]represents a significant leap forward in gravitational wave detection,advancing our understanding of gravitational *** introducing a fourth spacecraft to form a tetrahedral configuration,TEgO enhances traditional triangular setups,enabling the simultaneous sensitivity to all six gravitational wave polarization modes,including those not predicted by general relativity.
Carbon emissions resultingfrom energy consumption have become a pressing issue for governments *** estimation of carbon emissions using satellite remote sensing data has become a crucial research *** studies relied o...
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Carbon emissions resulting from energy consumption have become a pressing issue for governments *** estimation of carbon emissions using satellite remote sensing data has become a crucial research *** studies relied on statistical regression models that failed to capture the complex nonlinear relationships between carbon emissions and characteristic *** this study,we propose a machine learning algorithm for carbon emissions,a Bayesian optimized Xgboost regression model,using multi-year energy carbon emission data and nighttime lights(NTL)remote sensing data from Shaanxi Province,*** results demonstrate that the Xgboost algorithm outperforms linear regression and four other machine learning models,with an R^(2)of 0.906 and RMSE of *** observe an annual increase in carbon emissions,with high-emission counties primarily concentrated in northern and central Shaanxi Province,displaying a shift from discrete,sporadic points to contiguous,extended spatial *** autocorrelation clustering reveals predominantly high-high and low-low clustering patterns,with economically developed counties showing high-emission clustering and economically relatively backward counties displaying low-emission *** findings show that the use of NTL data and the Xgboost algorithm can estimate and predict carbon emissionsmore accurately and provide a complementary reference for satellite remote sensing image data to serve carbon emission monitoring and *** research provides an important theoretical basis for formulating practical carbon emission reduction policies and contributes to the development of techniques for accurate carbon emission estimation using remote sensing data.
Dear Editor,This letter investigates predefined-time optimization problems(OPs) of multi-agent systems(MASs), where the agent of MASs is subject to inequality constraints, and the team objective function accounts for ...
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Dear Editor,This letter investigates predefined-time optimization problems(OPs) of multi-agent systems(MASs), where the agent of MASs is subject to inequality constraints, and the team objective function accounts for impulse effects. firstly, to address the inequality constraints,the penalty method is introduced. Then, a novel optimization strategy is developed, which only requires that the team objective function be strongly convex.
As one of the main characteristics of atmospheric pollutants,PM_(2.5) severely affects human health and has received widespread attention in recent *** to predict the variations of PM_(2.5) concentrations with high ac...
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As one of the main characteristics of atmospheric pollutants,PM_(2.5) severely affects human health and has received widespread attention in recent *** to predict the variations of PM_(2.5) concentrations with high accuracy is an important *** PM_(2.5) monitoring stations in Xinjiang Uygur Autonomous Region,China,are unevenly distributed,which makes it challenging to conduct comprehensive analyses and ***,this study primarily addresses the limitations mentioned above and the poor generalization ability of PM_(2.5) concentration prediction models across different monitoring *** chose the northern slope of the Tianshan Mountains as the study area and took the January−December in 2019 as the research *** the basis of data from 21 PM_(2.5) monitoring stations as well as meteorological data(temperature,instantaneous wind speed,and pressure),we developed an improved model,namely gCN−TCN−AR(where gCN is the graph convolution network,TCN is the temporal convolutional network,and AR is the autoregression),for predicting PM_(2.5) concentrations on the northern slope of the Tianshan *** gCN−TCN−AR model is composed of an improved gCN model,a TCN model,and an AR *** results revealed that the R2 values predicted by the gCN−TCN−AR model at the four monitoring stations(Urumqi,Wujiaqu,Shihezi,and Changji)were 0.93,0.91,0.93,and 0.92,respectively,and the RMSE(root mean square error)values were 6.85,7.52,7.01,and 7.28μg/m^(3),*** performance of the gCN−TCN−AR model was also compared with the currently neural network models,including the gCN−TCN,gCN,TCN,Support Vector Regression(SVR),and *** gCN−TCN−AR outperformed the other current neural network models,with high prediction accuracy and good stability,making it especially suitable for the predictions of PM_(2.5)*** study revealed the significant spatiotemporal variations of PM_(2.5)***,the PM_(2.5) concentrations exhibited cle
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