With the rapid development and widespread deployment of Internet of Things devices, existing networks face significant challenges in meeting the demands of emerging large-scale applications. In this article, we propose a novel paradigm to address these challenges by decomposing large applications/services into lightweight microservices (MSs) distributed among small base stations (SBSs), each responsible for specific functions. Upon receiving a service request, a macro base station (MBS) invokes a series of SBSs that cache the required MSs to execute the associated computational tasks. The computed results are then returned to the MBS, which integrates and delivers the final result to the user. Under this framework, we investigate the joint problem of MS caching, computation task assignment, and computing resource allocation, aiming to minimize the total energy consumption. Various practical constraints, such as users’ latency requirements, and the limited caching and computing resources of SBSs are taken into account. To facilitate the analysis, we transform the original minimization problem into an equivalent problem focusing on MS computation task assignment and computing resource allocation, which remains NP-hard. To tackle this challenge efficiently, we devise a two-stage method. In the first stage, we derive a closed-form expression for the computing resource allocation policy based on the MS computation task assignment. Subsequently, we introduce a two-side swapping oriented approach to explore an improved MS computation task assignment strategy. In addition, we propose the use of exhaustive and simulated annealing algorithms to approach the optimal and near-optimal solutions, respectively. Extensive simulation results demonstrate that our proposed algorithm achieves close-to-optimal performance and outperforms benchmark schemes significantly.

MOTIVATION: Imaging genetics integrates imaging and genetic techniques to examine how genetic variations influence the function and structure of organs like the brain or heart, providing insights into their impact on behavior and disease phenotypes. The use of organ-wide imaging endophenotypes has increasingly been used to identify potential genes associated with complex disorders. However, analyzing organ-wide imaging data alongside genetic data presents two significant challenges: high dimensionality and complex relationships. To address these challenges, we propose a novel, nonlinear inference framework designed to partially mitigate these issues. RESULTS: We propose a functional partial least squares through distance covariance (FPLS-DC) framework for efficient genome wide analyses of imaging phenotypes. It consists of two components. The first component utilizes the FPLS-derived base functions to reduce image dimensionality while screening genetic markers. The second component maximizes the distance correlation between genetic markers and projected imaging data, which is a linear combination of the FPLS-basis functions, using simulated annealing algorithm. In addition, we proposed an iterative FPLS-DC method based on FPLS-DC framework, which effectively overcomes the influence of inter-gene correlation on inference analysis. We efficiently approximate the null distribution of test statistics using a gamma approximation. Compared to existing methods, FPLS-DC offers computational and statistical efficiency for handling large-scale imaging genetics. In real-world applications, our method successfully detected genetic variants associated with the hippocampus, demonstrating its value as a statistical toolbox for imaging genetic studies. AVAILABILITY AND IMPLEMENTATION: The FPLS-DC method we propose opens up new research avenues and offers valuable insights for analyzing functional and high-dimensional data. In addition, it serves as a useful tool for scientific analysis in practical applications within the field of imaging genetics research. The R package FPLS-DC is available in Github: https://github.com/BIG-S2/FPLSDC.

Abstract Caching popular contents at base stations (BSs) can effectively avoid redundant data traffic and improve backhaul capacity. Techniques such as multicast (MC) and non‐orthogonal multiple access (NOMA) can significantly improve spectral efficiency by delivering popular contents to multiple users using a single channel. Therefore, we propose a cooperative transmission scheme of MC and NOMA in cache‐enabled wireless cellular networks. First, we derive the probability mass function (PMF) of the number of channels in the MC mode as well as the joint PMF of the number of channels and number of NOMA users in the NOMA mode. Second, we analyse the successful transmission probabilities of the two modes by utilising tools from stochastic geometry. Finally, with the derived probabilities using the two modes, we obtain the total successful transmission probability based on probability theory. The quasi‐closed form expression of the successful transmission probability is obtained in a special case, where the noise is neglected, and the path‐loss exponent is set to be four. Simulation results validate the accuracy of the analysis and demonstrate the performance gain due to the proposed cooperative transmission scheme of MC and NOMA.

Soil moisture is a key component of land surface parameterization. The triangle/trapezoid feature space can be used to monitor soil moisture effectively. This research aims to use enhanced vegetation index (EVI) as an alternative for the normalized difference vegetation index (NDVI) in estimation of temperature vegetation dryness index (TVDI) to improve its ability of retrieving soil moisture and to discuss the flexibility of EVI in retrieval of soil moisture. The result shows that LST-EVI has higher R2in linear regression and more significant in validation at depth of 0–10cm than LST-NDVI. Influence caused by altitude for LST-EVI in monitoring soil moisture will be discussed in this study. In comparison with precipitation, LST-EVI and soil moisture shows a better mirror symmetry in dry season than in rainy season.

Land surface temperature (LST) is a very key variable for land surface process research. However, the retrieval of LST is still underdetermined issue because of the fact that the unknowns are always more than the measurements even the atmospheric condition can be acquired completely. Currently, Support Vector Machine (SVM) as an effective machine learning tool has been used widely in the domain of quantitative remote sensing because its optimization and generalization. This paper used SVM to retrieve LST based on only one thermal band in HJ-1B satellite launched by China. The radiance and water vapor content were selected as the independent variables. The validation result indicates that the errors of the SVM-MOD07 are lower than the Qin's-MOD07. Additionally, the sensitivity analysis indicates that when the errors of the water vapor content increase, the errors for the SVM model change insignificantly. In the end the SVM model was applied in Beijing area.