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As an emerging and promising computing paradigm in the Internet of things (IoT),edge computing can significantly reduce energy consumption and enhance computation capabilityfor resource-constrained IoT devices. Computation offloading has recently received considerableattention in edge computing. Many existing studies have investigated the computation offloadingproblem with independent computing tasks. However, due to the inter-task dependency in variousdevices that commonly happens in IoT systems, achieving energy-efficient computation offloadingdecisions remains a challengeable problem. In this paper, a cloud-assisted edge computing frameworkwith a three-tier network in an IoT environment is introduced. In this framework, we first formulatedan energy consumption minimization problem as a mixed integer programming problem consideringtwo constraints, the task-dependency requirement and the completion time deadline of the IoT service.To address this problem, we then proposed an Energy-efficient Collaborative Task ComputationOffloading (ECTCO) algorithm based on a semidefinite relaxation and stochastic mapping approachto obtain strategies of tasks computation offloading for IoT sensors. Simulation results demonstratedthat the cloud-assisted edge computing framework was feasible and the proposed ECTCO algorithmcould effectively reduce the energy cost of IoT sensors.

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The aim of this study was to characterize a Triticum aestivum-Psathyrostachys huashanica Keng (2n = 2x = 14, NsNs) disomic addition line 2-1-6-3. Individual line 2-1-6-3 plants were analyzed using cytological, genomic in situ hybridization (GISH), EST-SSR, and EST-STS techniques. The alien addition line 2-1-6-3 was shown to have two P. huashanica chromosomes, with a meiotic configuration of 2n = 44 = 22 II. We tested 55 EST-SSR and 336 EST-STS primer pairs that mapped onto seven different wheat chromosomes using DNA from parents and the P. huashanica addition line. One EST-SSR and nine EST-STS primer pairs indicated that the additional chromosome of P. huashanica belonged to homoeologous group 7, the diagnostic fragments of five EST-STS markers (BE404955, BE591127, BE637663, BF482781 and CD452422) were cloned, sequenced and compared. The results showed that the amplified polymorphic bands of P. huashanica and disomic addition line 2-1-6-3 shared 100% sequence identity, which was designated as the 7Ns disomic addition line. Disomic addition line 2-1-6-3 was evaluated to test the leaf rust resistance of adult stages in the field. We found that one pair of the 7Ns genome chromosomes carried new leaf rust resistance gene(s). Moreover, wheat line 2-1-6-3 had a superior numbers of florets and grains per spike, which were associated with the introgression of the paired P. huashanica chromosomes. These high levels of disease resistance and stable, excellent agronomic traits suggest that this line could be utilized as a novel donor in wheat breeding programs.

Multivariate time series forecasting recently has received extensive attention with its wide application in finance, transportation, environment, and so on. However, few of the currently developed models have considered the impact of noise on prediction. Since multivariate time series contains multiple subsequences with strong nonlinear fluctuations, it is also difficult to obtain satisfactory prediction results. In this paper, aiming at improving prediction performance, we have proposed a novel ensemble threephase model called adaptive noise reducer-stacked auto-encoder-validating-AdaBoost-based long shortterm memory (ANR-SAE-VALSTM). We start with an introduction of a novel ANR for time series noise elimination. The SAEs are then used to extract features from the de-noised multivariate time series. Finally, we feed the de-noised features into the VALSTM to train an ensemble over-fitting prevention predictor. The proposed model is employed on the Beijing PM2.5 dataset and GEFCom2014 Electricity Price dataset. Compared with other popular models, the proposed model has achieved the best prediction performance in all prediction horizons. In addition, a careful ablation study is conducted to demonstrate the efficiency of our model design. INDEX TERMS Multivariate time series forecasting, adaptive noise reducer, stacked auto-encoders, long short-term memory, validating AdaBoost algorithm.

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Measurements of K-shell ionization cross sections of Fe and Mn targets by the impact of electrons of 6-25 keV energy have been carried out. In the experiments thin targets with thick aluminium foil substrates were used. The influence on the measurements of electrons reflected from the aluminium substrate has been evaluated and corrected by a detailed electron transport calculation. It is shown that the corrected K-shell ionization cross sections of Mn are in good agreement with existing experimental data. For Mn and Fe, the corrected measured cross sections of K-shell ionization have been compared with the results of the empirical formula given by Casnati et al.

Accumulating evidence has supported that osteosarcoma is heterogeneous, and several subtypes have been identified based on genomic profiling. Immunotherapy is revolutionizing cancer treatment and is a promising therapeutic strategy. In contrast, few studies have identified osteosarcoma classification based on immune biosignatures, which offer the optimal stratification of individuals befitting immunotherapy. Here, we classified osteosarcoma into two clusters: immunity high and immunity low using the single-sample gene-set enrichment analysis and unsupervised hierarchical clustering. Immunity_H subtype was associated with high immune cells infiltration, a favorable prognosis, benefit to immunotherapy, high human leukocyte antigen gene expression, and activated immune signal pathway indicating an immune-hot phenotype. On the contrary, the Immunity_L subtype was correlated with low immune cell infiltration, poor prognosis, and cancer-related pathway, indicating an immune-cold phenotype. We also identified TYROBP as a key immunoregulatory gene associated with CD8+ T cell infiltration by multiplex immunohistochemistry. Finally, we established an immune-related prognostic model that predicted the survival time of osteosarcoma. In conclusion, we established a new classification system of osteosarcoma based on immune signatures and identified TYROBP as a key immunoregulatory gene. This stratification had significant clinical outcomes for estimating prognosis, as well as the immunotherapy of osteosarcoma patients.