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The issues of high costs and sophisticated circuit structures limit applicability of traditional chipped sensors. This paper innovatively designs a chipless sensor based on the split-ring resonator combined with temperature-sensitive material as the substrate. The alumina ceramics are used as the sensitive material for reflecting the environment temperature in accordance with its characteristic that its dielectric constant increases monotonically with rising temperature. The simulation demonstrates that the resonant frequency of the sensor monotonically decreases from 8.58 GHz to 8.22 GHz with an offset of 0.36 GHz and a sensitivity of 0.9 MHz/°C for a variation from 500 °C and 900 °C. The sensor designed in this paper has good resonance characteristics, is wireless, passive, and low cost, has a planar structure, and is suitable for various harsh environments.

SummaryAlthough it is possible to apply traditional optimization algorithms to determine the Pareto front of a multiobjective optimization problem, the computational cost is extremely high when the objective function evaluation requires solving a complex reservoir simulation problem and optimization cannot benefit from adjoint-based gradients. This paper proposes a novel workflow to solve bi-objective optimization problems using the distributed quasi-Newton (DQN) method, which is a well-parallelized and derivative-free optimization (DFO) method. Numerical tests confirm that the DQN method performs efficiently and robustly.The efficiency of the DQN optimizer stems from a distributed computing mechanism that effectively shares the available information discovered in prior iterations. Rather than performing multiple quasi-Newton optimization tasks in isolation, simulation results are shared among distinct DQN optimization tasks or threads. In this paper, the DQN method is applied to the optimization of a weighted average of two objectives, using different weighting factors for different optimization threads. In each iteration, the DQN optimizer generates an ensemble of search points (or simulation cases) in parallel, and a set of nondominated points is updated accordingly. Different DQN optimization threads, which use the same set of simulation results but different weighting factors in their objective functions, converge to different optima of the weighted average objective function. The nondominated points found in the last iteration form a set of Pareto-optimal solutions. Robustness as well as efficiency of the DQN optimizer originates from reliance on a large, shared set of intermediate search points. On the one hand, this set of searching points is (much) smaller than the combined sets needed if all optimizations with different weighting factors would be executed separately; on the other hand, the size of this set produces a high fault tolerance, which means even if some simulations fail at a given iteration, the DQN method’s distributed-parallel information-sharing protocol is designed and implemented such that the optimization process can still proceed to the next iteration.The proposed DQN optimization method is first validated on synthetic examples with analytical objective functions. Then, it is tested on well-location optimization (WLO) problems by maximizing the oil production and minimizing the water production. Furthermore, the proposed method is benchmarked against a bi-objective implementation of the mesh adaptive direct search (MADS) method, and the numerical results reinforce the auspicious computational attributes of DQN observed for the test problems.To the best of our knowledge, this is the first time that a well-parallelized and derivative-free DQN optimization method has been developed and tested on bi-objective optimization problems. The methodology proposed can help improve efficiency and robustness in solving complicated bi-objective optimization problems by taking advantage of model-based search algorithms with an effective information-sharing mechanism.NOTE: This paper is also published as part of the 2021 SPE Reservoir Simulation Conference Special Issue.

LINC00467 was reported as an oncogenic gene in different types of human cancers. In this study, we investigated the molecular mechanisms of LINC00467 in the tumorigenesis of laryngeal squamous cell cancer (LSCC). RT-qPCR was utilized to detect the mRNA expression of genes, and western blot assay was used to determine the protein levels of TNF alpha-induced protein 3 (TNFAIP3). The cell viability was detected by CCK-8 assay. Transwell assays were conducted to determine the cell migration and invasion of LSCC cells, and the cell cycle was assessed by flow cytometry. The association between paired box 5 (PAX5), LINC00467, miR-4735-3p, and TNFAIP3 was verified using ChIP, RNA pull-down, or luciferase reporter assays. In our study, we found that LINC00467 was upregulated in LSCC, and knockdown of LINC00467 suppressed cell viability and metastasis of LSCC. Besides, LINC00467 transcription could be activated by PAX5 in LSCC. Furthermore, LINC00467 acted as competitive endogenous RNA (ceRNA) for miR-4735-3p to accelerate LSCC progression. In the meantime, TNFAIP3 was identified as a downstream gene of miR-4735-3p. Finally, TNFAIP3 overexpression could overturn the effects of miR-4735-3p mimic on LSCC cellular activities. In conclusion, our results demonstrated that PAX5-induced LINC00467 facilitated LSCC progression by inhibiting miR-4735-3p to increase TNFAIP3 expression.

Recently, the extremal problem of the spectral radius in the class of complements of trees, unicyclic graphs, bicyclic graphs and tricyclic graphs had been studied widely. In this paper, we extend the largest ordering of A α -spectral radius among all complements of bicyclic and tricyclic graphs with n vertices, respectively.

Local breeding of growing modes (LBGM) is a method used to generate initial condition perturbation (ICP) for convection-permitting ensemble forecasts. Equal weights (EWs) are usually presumed in LBGM during the localization of ICP, without considering different contributions of the grid points within the local radius. To address this problem, Gaussian weights (GWs) are proposed in this study, which can accommodate the varied influences of the grids inside the local radius on the central grid through a Gaussian function. Specifically, two convection-permitting ensemble forecast experiments based on LBGM with GWs and EWs are compared and analyzed respectively for two squall line cases. The results showed that the use of the GWs intensified the local characteristics of the ICP and made the distribution of the ICP fields more flow-dependent. Kinetic energy spectrum of the ICP indicated that there could be more large-scale information in the ICP by using the GWs. In addition, mesoscale information also improved slightly. For forecast of nonprecipitation variables, GWs improved the relationship between the root-mean-square error and the spread and contributed to the forecasting accuracy of wind, temperature, geopotential height, and humidity. For the precipitation forecast, GWs simulated the precipitation structure successfully and provided better probability forecasting during the evolution of the two squall line processes than the EWs.

This article investigates the problem of synchronous randomized neighbor discovery with directional antennas. Due to the long tail effect, it will take long time to discover the last few neighbors, which increases overall neighbor discovery time. This effect is due to small proportion of remaining undiscovered neighbors. Moreover, improper choices of reception probabilities make the discovery even worse. In this article, a cognitive framework is proposed to minimize the expectation of neighbor discovery time. We present a scheme in which reception probabilities are dynamically adjusted. We consider an ideal scenario and a practical scenario. In an ideal scenario where perfect information about the number of neighbors is available, reception probabilities are adjusted according to the number of neighbors. A method of dynamic programming is used to recursively calculate the optimal reception probabilities. In an actual scenario where perfect information about number of neighbors is unavailable, a neighbor estimation method based on maximum-likelihood estimation is executed before probability adjustment. Simulation results show that when perfect information about neighbor is available and total transmission probability is within a proper range (between 0.1 and 0.2), the average neighbor discovery time can be significantly reduced (by 38% to 43%, respectively) compared with an existing probability-fixed scheme. With imperfect information, the scheme also works well and realizes appreciable reduction in average neighbor discovery time compared with existing self-adaptive schemes.

The paraffin molecules in crude oil crystalize and precipitate out as the oil cools, resulting in a sharp increase in oil viscosity and further the gelation of the oil. The waxy crude oil gel exhibits complex rheological behaviors, such as viscoelasticity, yield stress, and thixotropy, posing challenges to the flow assurance of pipelines. Previous studies have verified that applying a high-voltage electric field to a flowing liquid oil can significantly reduce its viscosity. In this work, the electrorheological behaviors of waxy crude oil gel are explored by a stress-controlled rheometer equipped with in situ DC electric field manipulation. It is reported for the first time that the gel structure can be significantly weakened upon the application of an electric field for a duration of not less than 10 s, with the yield stress, storage modulus, loss modulus, and apparent viscosity reduced by up to 90%. In addition, the gel structure fracture is observed to transit from brittle-like to ductile-like. A stronger structure weakening effect can be obtained at a higher field strength and lower temperature. The structure weakening effect induced by the electric field can be maintained for a certain period of time after the removal of the electric field, with a recovery of yield stress and moduli less than 10% in 48 h. We hold the view that the electrorheological behaviors of waxy oil should be attributed to the weakened attraction between wax particles induced by the electric field.

Highly sensitive detection of DNA is of great importance for the detection of genetic damage and errors for the diagnosis of many diseases. Traditional highly sensitive organic electrochemical transistor (OECT)-based methods mainly rely on good conductivity materials, which may be limited by complex synthesis and modification steps. In this work, DNA biosensor based on OECT and hybridization chain reaction (HCR) signal amplification was demonstrated for the first time. Au nanoparticles were electrochemically deposited on the Au gate electrode to increase the surface area. Then, the HCR products, long negatively charged double-stranded DNA, were connected to the target by hybridization, which can increase the effective gate voltage offset of OECT. This sensor exhibited high sensitivity and even 0.1 pM target DNA could be directly detected with a significant voltage shift. In addition, it could discriminate target DNA from the mismatched DNA with good selectivity. This proposed method based on HCR in DNA detection exhibited an efficient amplification performance on OECT, which provided new opportunities for highly sensitive and selective detection of DNA.

Treatment with pour point depressant (PPD) can significantly improve the cold flowability of waxy crude oil, enabling the safe and efficient transportation of waxy crude oils through pipelines. It ...

To quantify the intensity of squall line in mid-latitudes, the author recently proposed a squall line intensity assessment method based on cold pool, which provides a measure of squall line intensity.The disturbance potential temperature density is calculated by using the potential temperature, water vapor and all kinds of water condensate output from the numerical weather forecast model, and the boundary of the cold pool is judged according to the disturbance potential temperature density less than -2K. Based on the contour surface buoyancy, the high surface buoyancy is calculated according to the disturbance potential temperature density, and then the strength of the cold pool is calculated. In this method, the intensity of squall line is analyzed comprehensively by principal component analysis, combined with the weather phenomena accompanied by squall line occurrence, such as cold pool intensity, surface wind speed, ground pressure variation, surface temperature variation, simulated radar echo and so on. The above analysis is the local intensity on different grid points when the squall line occurs, and the overall squall line intensity is obtained by accumulating the local intensity in the squall line range.The method is verified by the model output data of a squall line process occurred in northern Jiangsu on May 16, 2013. The results show that the distribution of the local squall line intensity is coupled with the surface wind field and heavy precipitation. The intensity evolution of the overall squall line reaches the peak in a short time and then decreases, which corresponds to the life history of the birth, development, maturity and dissipation of the squall line, and also reflects the characteristics of the short life history of the squall line developing rapidly and then dissipating. This method provides technical support for the forecast of squall line and the emergency plan issued by meteorological department.Acknowledgements. This research was supported by the National Natural Science Foundation of China (Grant Nos. 41975128 and 42075053).Keywords: squall line, intensity, assessment method, disturbance potential temperature density

A new dual-recognition fluorescent biosensor for circulating tumor DNA (ctDNA) detection has been developed, which combines the clamping function of peptide nucleic acid (PNA) and terminal protection of small-molecule-linked DNA (TPSMLD). Taking the tumor-specific E542K mutation and methylation of the PIK3CA gene as the target ctDNA, a low detection limit of 0.3161 pM ctDNA is achieved with good selectivity. This study not only offers a sensitive, selective and accurate ctDNA detection method, but can also be used to detect the target in complex biological samples.

Waxy crude oil presents poor flowability and complicated rheological behaviors near and below its gelation temperature. In recent years, high voltage direct current electric field has emerged as a novel approach to significantly reduce the viscosity of waxy crude oil below its wax appearance temperature, but the effect of electrical treatment on the structural behaviors of gelled waxy crude oil remains unknown. In the present study, the viscoelasticity, yielding behavior, and thixotropy of gelled waxy crude oil experienced electrically-treatment were investigated over a range of electrical field strength of 0–0.8 kV/mm and treatment temperature between the wax appearance temperature and the pour point. We demonstrate for the first time that the structural strength of gelled waxy crude oil can be significantly weakened after electrical treatment below its wax appearance temperature. The magnitude of the reduction increases with the electric field strength increasing and treatment temperature decreasing. However, no significant influence was found on the wax appearance temperature, wax disappearance temperature, and the amount of precipitated and dissolved wax, revealing the phase equilibrium of wax in crude oil not affected. The investigation of the wax crystal microscopy shows that the wax crystals in the electrically-treated waxy gels are generally larger, and have larger aspect ratio and boundary box fractal dimension, which indicates that the size and morphology of wax crystals are varied by the influence of the electrical treatment.

MicroRNAs play important roles in disease diagnosis and therapy. However, current methods for microRNA detection suffer from low sensitivity and cannot directly detect short microRNAs. Herein, we have developed a highly sensitive and selective fluorescent method for direct microRNA detection by combining the duplex-specific nuclease-assisted recycling amplification and the nicking enzyme-powered three-dimensional DNA walker. Target microRNA initiates duplex-specific nuclease-assisted recycling amplification, releasing numerous bipedal walking strands. The released bipedal walking strands hybridize with carboxyfluorescein-labeled track DNA and form nicking recognition site. Driven by the hydrolysis of the nicking enzyme, the bipedal walking strand autonomously moves along the track strand, releasing a large number of carboxyfluorescein-labeled DNA fragments and generating obvious fluorescence signals. This dual-signal amplification method can directly detect microRNA 21 as low as 130 fM and has good selectivity. The proposed method is not only simple for nucleic acid design, but also can be used as a universal method for the highly sensitive detection of all RNAs.

Although it is possible to apply traditional optimization algorithms to determine the Pareto front of a multi-objective optimization problem, the computational cost is extremely high, when the objective function evaluation requires solving a complex reservoir simulation problem and optimization cannot benefit from adjoint-based gradients. This paper proposes a novel workflow to solve bi-objective optimization problems using the distributed quasi-Newton (DQN) method, which is a well-parallelized and derivative-free optimization (DFO) method. Numerical tests confirm that the DQN method performs efficiently and robustly. The efficiency of the DQN optimizer stems from a distributed computing mechanism which effectively shares the available information discovered in prior iterations. Rather than performing multiple quasi-Newton optimization tasks in isolation, simulation results are shared among distinct DQN optimization tasks or threads. In this paper, the DQN method is applied to the optimization of a weighted average of two objectives, using different weighting factors for different optimization threads. In each iteration, the DQN optimizer generates an ensemble of search points (or simulation cases) in parallel and a set of non-dominated points is updated accordingly. Different DQN optimization threads, which use the same set of simulation results but different weighting factors in their objective functions, converge to different optima of the weighted average objective function. The non-dominated points found in the last iteration form a set of Pareto optimal solutions. Robustness as well as efficiency of the DQN optimizer originates from reliance on a large, shared set of intermediate search points. On the one hand, this set of searching points is (much) smaller than the combined sets needed if all optimizations with different weighting factors would be executed separately; on the other hand, the size of this set produces a high fault tolerance. Even if some simulations fail at a given iteration, DQN’s distributed-parallel information-sharing protocol is designed and implemented such that the optimization process can still proceed to the next iteration. The proposed DQN optimization method is first validated on synthetic examples with analytical objective functions. Then, it is tested on well location optimization problems, by maximizing the oil production and minimizing the water production. Furthermore, the proposed method is benchmarked against a bi-objective implementation of the MADS (Mesh Adaptive Direct Search) method, and the numerical results reinforce the auspicious computational attributes of DQN observed for the test problems. To the best of our knowledge, this is the first time that a well-parallelized and derivative-free DQN optimization method has been developed and tested on bi-objective optimization problems. The methodology proposed can help improve efficiency and robustness in solving complicated bi-objective optimization problems by taking advantage of model-based search optimization algorithms with an effective information-sharing mechanism.

Impedance spectroscopy (IS) is a widely used nondestructive and high-throughput experimental technique for the characterization of electrical properties of materials. In the petroleum industry, IS ...

Summary It has been demonstrated that the Gaussian-mixture-model (GMM) fitting method can construct a GMM that more accurately approximates the posterior probability density function (PDF) by conditioning reservoir models to production data. However, the number of degrees of freedom (DOFs) for all unknown GMM parameters might become huge for large-scale history-matching problems. A new formulation of GMM fitting with a reduced number of DOFs is proposed in this paper to save memory use and reduce computational cost. The performance of the new method is benchmarked against other methods using test problems with different numbers of uncertain parameters. The new method performs more efficiently than the full-rank GMM fitting formulation, reducing the memory use and computational cost by a factor of 5 to 10. Although it is less efficient than the simple GMM approximation dependent on local linearization (L-GMM), it achieves much higher accuracy, reducing the error by a factor of 20 to 600. Finally, the new method together with the parallelized acceptance/rejection (A/R) algorithm is applied to a synthetic history-matching problem for demonstration.

Electrical treatment is emerging as an efficient approach to improve the cold flowability of crude oils. In this work, waxy model oils under static state were electrically treated by an in-house apparatus. The influence of asphaltenes, the most aromatic of the heaviest components of crude oil, on the performance of electrical treatment was systematically examined for the first time. The presence of asphaltenes was found to significantly undermine the effectiveness of electrical treatment on waxy oils, and this negative influence became more pronounced at higher asphaltene contents. The effect of asphaltenes was largely independent on their aggregation state. Interestingly, although asphaltenes generally have a negative effect on electrical treatment of waxy oils, increasing polarity of asphaltenes alleviates this unfavorable effect. The results from microscopic analysis suggest that the less pronounced change in the size distribution of wax crystals might be one of the reasons for the unfavorable effects of asphaltenes on electrical treatment of waxy oils.

For most history matching problems, the posterior probability density function (PDF) may have multiple local maxima, and it is extremely challenging to quantify uncertainty of model parameters and production forecasts by conditioning to production data. In this paper, a novel method is proposed to improve the accuracy of Gaussian mixture model (GMM) approximation of the complex posterior PDF by adding more Gaussian components. Simulation results of all reservoir models generated during the history matching process, e.g., using the distributed Gauss-Newton (DGN) optimizer, are used as training data points for GMM fitting. The distance between the GMM approximation and the actual posterior PDF is estimated by summing up the errors calculated at all training data points. The distance is an analytical function of unknown GMM parameters such as covariance matrix and weighting factor for each Gaussian component. These unknown GMM parameters are determined by minimizing the distance function. A GMM is accepted if the distance is reasonably small. Otherwise, new Gaussian components will be added iteratively to further reduce the distance until convergence. Finally, high-quality conditional realizations are generated by sampling from each Gaussian component in the mixture, with the appropriate relative probability. The proposed method is first validated using nonlinear toy problems and then applied to a history-matching example. GMM generates better samples with a computational cost comparable to or less than other methods we tested. GMM samples yield production forecasts that match production data reasonably well in the history-matching period and are consistent with production data observed in the blind test period.

Summary Uncertainty quantification of production forecasts is crucially important for business planning of hydrocarbon–field developments. This is still a very challenging task, especially when subsurface uncertainties must be conditioned to production data. Many different approaches have been proposed, each with their strengths and weaknesses. In this work, we develop a robust uncertainty–quantification work flow by seamless integration of a distributed–Gauss–Newton (GN) (DGN) optimization method with a Gaussian mixture model (GMM) and parallelized sampling algorithms. Results are compared with those obtained from other approaches. Multiple local maximum–a–posteriori (MAP) estimates are determined with the local–search DGN optimization method. A GMM is constructed to approximate the posterior probability–density function (PDF) by reusing simulation results generated during the DGN minimization process. The traditional acceptance/rejection (AR) algorithm is parallelized and applied to improve the quality of GMM samples by rejecting unqualified samples. AR–GMM samples are independent, identically distributed samples that can be directly used for uncertainty quantification of model parameters and production forecasts. The proposed method is first validated with 1D nonlinear synthetic problems with multiple MAP points. The AR–GMM samples are better than the original GMM samples. The method is then tested with a synthetic history–matching problem using the SPE01 reservoir model (Odeh 1981; Islam and Sepehrnoori 2013) with eight uncertain parameters. The proposed method generates conditional samples that are better than or equivalent to those generated by other methods, such as Markov–chain Monte Carlo (MCMC) and global–search DGN combined with the randomized–maximum–likelihood (RML) approach, but have a much lower computational cost (by a factor of five to 100). Finally, it is applied to a real–field reservoir model with synthetic data, with 235 uncertain parameters. A GMM with 27 Gaussian components is constructed to approximate the actual posterior PDF. There are 105 AR–GMM samples accepted from the 1,000 original GMM samples, and they are used to quantify the uncertainty of production forecasts. The proposed method is further validated by the fact that production forecasts for all AR–GMM samples are quite consistent with the production data observed after the history–matching period. The newly proposed approach for history matching and uncertainty quantification is quite efficient and robust. The DGN optimization method can efficiently identify multiple local MAP points in parallel. The GMM yields proposal candidates with sufficiently high acceptance ratios for the AR algorithm. Parallelization makes the AR algorithm much more efficient, which further enhances the efficiency of the integrated work flow.

In this study, a convection-allowing ensemble prediction experiment was conducted on a strong convective weather process, based on the local breeding growth mode (LBGM) method proposed according to the strongly local nature of the convective-scale weather system. A comparative analysis of the evolution characteristics of the initial perturbation was also performed, considering the results from the traditional breeding growth mode (BGM) method, to enhance understanding and application of this new initial perturbation generation method. The experimental results showed that LBGM results in the perturbation distribution exhibiting characteristics more evident of flow dependence, and an initial perturbation with greater definite kinetic significance was derived. Information entropy theory could well measure the amount of information contained in the perturbation distribution, indicating that the innovative initial perturbation generation method can increase the amount of local information associated with the initial perturbation. With regard to the physical perturbation quantities, the LBGM method can improve the dispersion of the ensemble prediction system, thereby solving the problem of insufficient ensemble spread of prediction systems obtained by the traditional BGM method. Simultaneously, the root-mean-square error of the prediction can be further reduced, and the predicted precipitation distribution is closer to the observed precipitation, thereby improving the prediction effect of the convection-allowing ensemble prediction. The LBGM method has advantages compared to the traditional method and provides a new theoretical basis for further development of initial perturbation technologies for convection-allowing ensemble prediction.

In this paper, we study the joint problem of multichannel selection and data scheduling for high-frequency (HF) communication under jamming environment. Prior anti-jamming work mainly discussed fixed transmission time and considered saturated scenarios in which the agent always had packets to transmit. But HF dynamic spectrum environment and time-varying communication demand make traditional anti-jamming methods ineffective. To cope with above challenge, dynamic transmission time and packet scheduling are considered in this manuscript. The transmitter selects the working channel and the number of packets to be transmitted according to current jamming environment and buffer state. Simultaneously, channel diversity which allows the transmitter to select multiple channels to send same packet is also considered to overcome the unreliable and unstable characteristics of HF channel. We formulate the decision making process as a Markov decision process (MDP) and propose an interference-aware reinforcement learning algorithm. The proposed algorithm combining Q learning with upper confidence bounds for trees (UCT) balances exploration and exploitation of action set. Simulation results show that higher network throughput and less packet loss are obtained by the proposed algorithm.

In the present work, we employed model oil systems to examine the effects of resins upon the gelation and crystallization of waxy oils. Two types of waxes were explored, namely, n-tetracosane (wax A) and a commercial wax with a melting temperature of 52–54 °C (wax B). The resins were extracted from a deoiled asphalt from Venezuelan residue by the saturates, aromatics, resins, and asphaltenes fractionation method. The results from negative-ion electrospray ionization Fourier transform ion cyclotron resonance-mass spectrometry suggest that resins consist of one to four fused benzenes rings or one to two fused naphthalene rings constructed by N1, N1O1, N1S1, N1O1S1, N1O2, O1, O1S1, and O2 class species containing stacking aromatic rings. For both types of waxes examined, adding resins to the waxy oils suppresses wax precipitation and modifies the morphology of wax crystals, which collectively leads to lower gelation temperature and lower yield stress. Up to 7 °C of reduction in gelation temperature, 60% of r...