In the presence of scale, dynamism, uncertainty and elasticity, cloud software engineers faces several challenges when modeling Quality of Service (QoS) for cloud-based software services. These ...challenges can be best managed through self-adaptivity because engineers' intervention is difficult, if not impossible, given the dynamic and uncertain QoS sensitivity to the environment and control knobs in the cloud. This is especially true for the shared infrastructure of cloud, where unexpected interference can be caused by co-located software services running on the same virtual machine; and co-hosted virtual machines within the same physical machine. In this paper, we describe the related challenges and present a fully dynamic, self-adaptive and online QoS modeling approach, which grounds on sound information theory and machine learning algorithms, to create QoS model that is capable to predict the QoS value as output over time by using the information on environmental conditions, control knobs and interference as inputs. In particular, we report on in-depth analysis on the correlations of selected inputs to the accuracy of QoS model in cloud. To dynamically selects inputs to the models at runtime and tune accuracy, we design self-adaptive hybrid dual-learners that partition the possible inputs space into two sub-spaces, each of which applies different symmetric uncertainty based selection techniques; the results of sub-spaces are then combined. Subsequently, we propose the use of adaptive multi-learners for building the model. These learners simultaneously allow several learning algorithms to model the QoS function, permitting the capability for dynamically selecting the best model for prediction on the fly. We experimentally evaluate our models in the cloud environment using RUBiS benchmark and realistic FIFA 98 workload. The results show that our approach is more accurate and effective than state-of-the-art modelings.
Aims. Large compact polycyclic aromatic hydrocarbon molecules (PAHs) present special interest in the astrochemical community. A key issue in analyses of large PAHs is understanding the effect that ...temperature and anharmonicity have on different vibrational bands, and thus interpreting the infrared (IR) spectra for molecules under various conditions. Methods. Because of the huge amount of interactions/resonances in large PAHs, no anharmonic IR spectrum can be produced with static/time-independent ab initio method, especially for the molecules with D6h symmetry, e.g., coronene and circumcoronene. In this work, we performed molecular dynamics simulations to generate anharmonic IR spectra of coronene and circumcoronene. Results. The method is validated for small PAHs, i.e., naphthalene and pyrene. We find that the semiempirical method PM3 produces accurate band positions with an error <5 cm−1. Furthermore, we calculate the spectra at multiple temperatures and find a clear trend toward band shifting and broadening.
Fractures are widely distributed in the upper crust, which is important to transport processes in groundwater, geothermal, and hydrocarbon reservoirs. Little attention has been paid to the ...heterogeneity and accuracy of upscaled equivalent fracture models (EFMs) regarding the complex fracture geometries during numerical reservoir simulation. This paper investigates how fracture geometric properties affect the accuracy of highly heterogeneous EFMs. An ensemble of synthetic fractured porous rocks is generated stochastically with varied fracture length, fracture density, and aperture-length correlation. Each realization is upscaled to EFM with the recently developed multiple boundary method. Flow problems are solved for EFMs and discrete fracture models (DFMs) simultaneously, and their flow rates are compared. The results demonstrate that the heterogeneity of EFMs decreases with the number and length of fractures and increases with the aperture-length correlation component. The flow rates calculated by EFMs fit well with those by DFMs with a slope around 1.08 and R2 coefficient above 0.98. The accuracy of the upscaled EFM decreases when the aperture-length correlation changes from sublinear to linear. The uncertainty of upscaling accuracy decreases with dimensionless fracture density. The findings of this study can help for a better understanding of the effect of the discrete fracture geometries on the performance of upscaled EFMs and choosing the appropriate models for different kinds of fractured reservoirs.
Owing to their high energy density and power density, supercapacitors exhibit great potential as high-performance energy sources for advanced technologies. Recently, carbon nanomaterials (especially, ...carbon nanotubes and graphene) have been widely investigated as effective electrodes in supercapacitors due to their high specific surface area, excellent electrical and mechanical properties. This article summarizes the recent progresses on the development of high-performance supercapacitors based on carbon nanomaterials and provides various rational concepts for materials engineering to improve the device performance for a large variety of potential applications, ranging from consumer electronics through wearable optoelectronics to hybrid electric vehicles.
Polarimetric synthetic aperture radar (PolSAR) image classification is an important application. Advanced deep learning techniques represented by deep convolutional neural network (CNN) have been ...utilized to enhance the classification performance. One current challenge is how to adapt deep CNN classifier for PolSAR classification with limited training samples, while keeping good generalization performance. This letter attempts to contribute to this problem. The core idea is to incorporate expert knowledge of target scattering mechanism interpretation and polarimetric feature mining to assist deep CNN classifier training and improve the final classification performance. A polarimetric-feature-driven deep CNN classification scheme is established. Both classical roll-invariant polarimetric features and hidden polarimetric features in the rotation domain are used to drive the proposed deep CNN model. Comparison studies validate the efficiency and superiority of the proposal. For the benchmark AIRSAR data, the proposed method achieves the state-of-the-art classification accuracy. Meanwhile, the convergence speed from the proposed polarimetric-feature-driven CNN approach is about 2.3 times faster than the normal CNN method. For multitemporal UAVSAR data sets, the proposed scheme achieves comparably high classification accuracy as the normal CNN method for train-used temporal data, while for train-not-used data it obtains an average of 4.86% higher overall accuracy than the normal CNN method. Furthermore, the proposed strategy can also produce very promising classification accuracy even with very limited training samples.
DOA Estimation Using Compressed Sparse Array Guo, Muran; Zhang, Yimin D.; Tao Chen
IEEE transactions on signal processing,
2018-Aug.1,-1, 2018-8-1, Letnik:
66, Številka:
15
Journal Article
Recenzirano
Odprti dostop
Sparse arrays, such as nested arrays and coprime arrays, can achieve a high number of degrees of freedom (DOFs) for direction of arrival (DOA) estimation with a reduced number of antennas. On the ...other hand, the compressive measurement method provides an effective way to reduce the number of frontend circuit chains. In this paper, we generalized current works on the two categories of methods to a compressed sparse array (CSA) scheme, which combines the compressive measurement method, and the sparse array together to significantly reduce the system complexity. After introducing the proposed scheme, the Cramér-Rao bound (CRB) of the proposed CSA scheme is derived. We then determine the corresponding existing conditions of the CRB, based on which the number of DOFs is derived and examined for the first time. It is proved that, for a CSA which compressed the output of an L-element sparse array to M <; L chains, a higher number of DOFs is obtained as compared to that of the M-element array with the same sparse structure. Furthermore, the DOA estimation accuracy using the M-chain CSA is higher than that using the M-element sparse array due to the extended array aperture. Numerical simulations verify the superiority of the proposed CSA scheme.
Hierarchical nano-architectures comprised of ultrathin ternary selenide （CoNiSe2） nanorods were directly grown on nickel foam （NF）. The integrated CoNiSe2/NF functions as a robust electrocatalyst ...with an extremely high activity and stability for emerging renewable energy technologies, and electrochemical oxygen and hydrogen evolution reactions （OER and HER, respectively）. The overpotentials required to deliver a current density of 100 mA·cm^-2 are as low as 307 and 170 mV for the OER and HER, respectively; therefore, the obtained CoNiSe2 is among the most promising earth-abundant catalysts for water splitting. Furthermore, our synthetic sample validates a two-electrode electrolyzer for reducing the cell voltage in the full water splitting reaction to 1.591 V to achieve a current density of 10 mA·cm^-2, which offers a novel inexpensive, integrated selenide/NF electrode for electrocatalytic applications.
Background and Aims
Somatic mutation R249S in TP53 is highly common in hepatocellular carcinoma (HCC). We aim to investigate the effects of R249S in ctDNA on the prognosis of HCC.
Methods
We analysed ...three cohorts including 895 HCC patients. TP53 mutation spectrum was examined by direct sequencing of genomic DNA from tissue specimens in HCC patients with hepatectomy (Cohort 1, N = 260). R249S and other recurrent missense mutations were assessed for their biological functions and associations with overall survival (OS) and progression‐free survival (PFS) of HCC patients in Cohort 1. R249S within circulating tumour DNA (ctDNA) was detected through droplet digital polymerase chain reaction (ddPCR) and its association with OS and PRS was analysed in HCC patients with (Cohort 2, N = 275) or without (Cohort 3, N = 360) hepatectomy.
Results
In Cohort 1, R249S occupied 60.28% of all TP53 mutations. Overexpression of R249S induced more serious malignant phenotypes than those of the other three identified TP53 recurrent missense mutations. Additionally, R249S, but not other missense mutations, was significantly associated with worse OS (P = .006) and PFS (P = .01) of HCC patients. Consistent with the results in Cohort 1, HCC patients in Cohorts 2 and 3 with R249S had worse OS (P = 8.291 × 10−7 and 2.608 × 10−7 in Cohorts 2 and 3, respectively) and PFS (P = 5.115 × 10−7 and 5.900 × 10−13 in Cohorts 2 and 3, respectively) compared to those without this mutation.
Conclusions
TP53 R249S mutation in ctDNA may serve as a promising prognosis biomarker for HCC patients with or without hepatectomy.
Tumor metastasis is a hallmark of cancer. The communication between cancer-derived exosomes and stroma plays an irreplaceable role in facilitating pre-metastatic niche formation and cancer ...metastasis. However, the mechanisms underlying exosome-mediated pre-metastatic niche formation during colorectal cancer (CRC) liver metastasis remain incompletely understood. Here we identified HSPC111 was the leading upregulated gene in hepatic stellate cells (HSCs) incubated with CRC cell-derived exosomes. In xenograft mouse model, CRC cell-derived exosomal HSPC111 facilitated pre-metastatic niche formation and CRC liver metastases (CRLM). Consistently, CRC patients with liver metastasis had higher level of HSPC111 in serum exosomes, primary tumors and cancer-associated fibroblasts (CAFs) in liver metastasis than those without. Mechanistically, HSPC111 altered lipid metabolism of CAFs by phosphorylating ATP-citrate lyase (ACLY), which upregulated the level of acetyl-CoA. The accumulation of acetyl-CoA further promoted CXCL5 expression and secretion by increasing H3K27 acetylation in CAFs. Moreover, CXCL5-CXCR2 axis reinforced exosomal HSPC111 excretion from CRC cells and promoted liver metastasis. These results uncovered that CRC cell-derived exosomal HSPC111 promotes pre-metastatic niche formation and CRLM via reprogramming lipid metabolism in CAFs, and implicate HSPC111 may be a potential therapeutic target for preventing CRLM.
Estimating equivalent permeability at grid block scale of numerical models is a critical issue for large-scale fractured porous rocks. However, it is difficult to constrain the permeability ...distributions for equivalent fracture models as these are strongly influenced by complex fracture properties. This study quantitatively investigated equivalent permeability distributions for fractured porous rocks, considering the impact of the correlated fracture aperture and length model. Two-dimensional discrete fracture models are generated with varied correlation exponent ranges from 0.5 to 1, which indicates different geomechanical properties of fractured porous rock. The equivalent fracture models are built by the multiple boundary upscaling method. Results indicate that the spatial distribution of equivalent permeability varied with the correlation exponent. When the minimum fracture length and the number of fractures increase, the process that the diagonal equivalent permeability tensor components change from a power law like to a lognormal like and to a normal-like distribution slows down as the correlation exponent increases. The average dimensionless equivalent permeability for the equivalent fracture models is well described by an exponential relationship with the correlation exponent. A power law model is built between the equivalent permeability of equivalent fracture models and fracture density of discrete fracture models for the correlated aperture-length models. The results demonstrate that both the fracture density and length-aperture model influence the equivalent permeability of equivalent fracture models interactively.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
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